modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Declan/FoxNews_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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}
} | 3 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon-notyet
- animal
widget:
- text: a renaissance painting of catloxi cat wearing a crown sitting on a throne,
elegant, close-up
---
# DreamBooth model for the catloxi concept trained by nicolasneubauer on the nicolasneubauer/loxi dataset.
This is a Stable Diffusion model fine-tuned on the catloxi concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of catloxi cat**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `cat` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('nicolasneubauer/catloxi-cat')
image = pipeline().images[0]
image
```
|
Declan/HuffPost_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Abhi03/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"])
```
|
Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 3 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Abhi03/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"])
```
|
Declan/Reuters_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
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}
} | 7 | null | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "PlanTL-GOB-ES/MLDoc"
- "text-classification"
datasets:
- "PlanTL-GOB-ES/MLDoc"
metrics:
- "f1"
model-index:
- name: roberta-base-bne-mldoc
results:
- task:
type: text-classification
dataset:
type: mldoc
name: PlanTL-GOB-ES/MLDoc
metrics:
- name: F1
type: f1
value: 0.9664
widget:
- ' FRANCFORT, 17 feb (Reuter) - La Bolsa de Francfort abrió la sesión de corros con baja por la caída del viernes en Wall Street y una toma de beneficios. El dólar ayudaba a apuntalar al mercado, que pronto podría reanudar su tendencia alcista. Volkswagen bajaba por los daños ocasionados por la huelga de camioneros en España. Preussag participaba en un joint venture de exploración petrolífera en Filipinas con Atlantic Richfield Co. A las 0951 GMT, el Dax 30 bajaba 10,49 puntos, un 0,32 pct, a 3.237,69 tras abrir a un máximo de 3.237,69. (c) Reuters Limited 1997. '
---
# Spanish RoBERTa-base trained on BNE finetuned for the Spanish portion of the MLDoc dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Training](#training)
- [Training data](#training-data)
- [Training procedure](#training-procedure)
- [Evaluation](#evaluation)
- [Evaluation](#evaluation)
- [Variable and metrics](#variable-and-metrics)
- [Evaluation results](#evaluation-results)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Citing information](#citing-information)
- [Disclaimer](#disclaimer)
</details>
## Model description
The **roberta-base-bne-mldoc** is a text classification model for the Spanish language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
The finetuning corpus consists of 14,458 news articles from Reuters classified in four categories: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets).
## Intended uses and limitations
**roberta-base-bne-mldoc** model can be used to classify texts into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). The model is limited by its training dataset (news stories) and may not generalize well for all use cases.
## How to use
Here is how to use this model:
```python
from transformers import pipeline
from pprint import pprint
nlp = pipeline("text-classification", model="PlanTL-GOB-ES/roberta-base-bne-mldoc")
example = ' FRANCFORT, 17 feb (Reuter) - La Bolsa de Francfort abrió la sesión de corros con baja por la caída del viernes en Wall Street y una toma de beneficios. El dólar ayudaba a apuntalar al mercado, que pronto podría reanudar su tendencia alcista. Volkswagen bajaba por los daños ocasionados por la huelga de camioneros en España. Preussag participaba en un joint venture de exploración petrolífera en Filipinas con Atlantic Richfield Co. A las 0951 GMT, el Dax 30 bajaba 10,49 puntos, un 0,32 pct, a 3.237,69 tras abrir a un máximo de 3.237,69. (c) Reuters Limited 1997. '
results = nlp(example)
pprint(results)
```
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
For training and evaluation we used the Spanish portion of the Multilingual Document Classification Corpus (MLDoc) [(Schwenk and Li, 2018)](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf), a cross-lingual document classification dataset covering 8 languages.
### Training procedure
The model was trained with a batch size of 32 and a learning rate of 1e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.
## Evaluation
### Variable and metrics
This model was finetuned maximizing F1.
## Evaluation results
We evaluated the *roberta-base-bne-mldoc* on the XNLI test set against standard multilingual and monolingual baselines:
| Model | MLDoc (F1) |
| ------------|:----|
| roberta-base-bne | 96.64 |
| roberta-large-bne | 97.02 |
| BETO | **97.14** |
| mBERT | 96.17 |
| BERTIN | 96.68 |
| ELECTRA | 95.65 |
For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
### Contact information
For further information, send an email to <[email protected]>
### Copyright
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
## Citing information
If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405):
```
@article{,
abstract = {We want to thank the National Library of Spain for such a large effort on the data gathering and the Future of Computing Center, a
Barcelona Supercomputing Center and IBM initiative (2020). This work was funded by the Spanish State Secretariat for Digitalization and Artificial
Intelligence (SEDIA) within the framework of the Plan-TL.},
author = {Asier Gutiérrez Fandiño and Jordi Armengol Estapé and Marc Pàmies and Joan Llop Palao and Joaquin Silveira Ocampo and Casimiro Pio Carrino and Carme Armentano Oller and Carlos Rodriguez Penagos and Aitor Gonzalez Agirre and Marta Villegas},
doi = {10.26342/2022-68-3},
issn = {1135-5948},
journal = {Procesamiento del Lenguaje Natural},
keywords = {Artificial intelligence,Benchmarking,Data processing.,MarIA,Natural language processing,Spanish language modelling,Spanish language resources,Tractament del llenguatge natural (Informàtica),Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural},
publisher = {Sociedad Española para el Procesamiento del Lenguaje Natural},
title = {MarIA: Spanish Language Models},
volume = {68},
url = {https://upcommons.upc.edu/handle/2117/367156#.YyMTB4X9A-0.mendeley},
year = {2022},
}
```
## Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence.
In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
</details>
|
Declan/Reuters_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xlsum-with-multi-news-10-epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xlsum-with-multi-news-10-epoch
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2332
- Rouge1: 31.4802
- Rouge2: 9.9475
- Rougel: 24.6687
- Rougelsum: 24.7013
- Gen Len: 18.8025
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7314 | 1.0 | 20543 | 2.3867 | 29.3997 | 8.2875 | 22.8406 | 22.8871 | 18.8204 |
| 2.6652 | 2.0 | 41086 | 2.3323 | 30.3992 | 8.9058 | 23.6168 | 23.6626 | 18.8447 |
| 2.632 | 3.0 | 61629 | 2.3002 | 30.8662 | 9.2869 | 24.0683 | 24.11 | 18.8122 |
| 2.6221 | 4.0 | 82172 | 2.2785 | 31.143 | 9.5737 | 24.3473 | 24.381 | 18.7911 |
| 2.5925 | 5.0 | 102715 | 2.2631 | 31.2144 | 9.6904 | 24.4419 | 24.4796 | 18.8133 |
| 2.5812 | 6.0 | 123258 | 2.2507 | 31.3371 | 9.7959 | 24.5801 | 24.6166 | 18.7836 |
| 2.5853 | 7.0 | 143801 | 2.2437 | 31.3593 | 9.8156 | 24.5533 | 24.5852 | 18.8103 |
| 2.5467 | 8.0 | 164344 | 2.2377 | 31.368 | 9.8807 | 24.6226 | 24.6518 | 18.799 |
| 2.5571 | 9.0 | 184887 | 2.2337 | 31.4356 | 9.9092 | 24.6543 | 24.6891 | 18.8075 |
| 2.5563 | 10.0 | 205430 | 2.2332 | 31.4802 | 9.9475 | 24.6687 | 24.7013 | 18.8025 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.1+cpu
- Datasets 2.8.0
- Tokenizers 0.10.3
|
Declan/Reuters_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 650.00 +/- 222.46
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gstaff -f logs/
python enjoy.py --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 gstaff -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 gstaff
```
## 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)])
```
|
Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 3 | 2022-12-27T02:10:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned-eurosat-albumentations
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9814814814814815
---
<!-- 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. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0671
- Accuracy: 0.9815
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1452 | 1.0 | 190 | 0.1335 | 0.97 |
| 0.0683 | 2.0 | 380 | 0.0825 | 0.9763 |
| 0.0584 | 3.0 | 570 | 0.0671 | 0.9815 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Declan/WallStreetJournal_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 7 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.37 +/- 53.68
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DeepChem/SmilesTokenizer_PubChem_1M | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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}
}
} | 227 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: ja
datasets:
- lmqg/qag_jaquad
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている6月28日は2人の14回目の結婚記念日であった。"
example_title: "Questions & Answers Generation Example 1"
model-index:
- name: lmqg/mt5-small-jaquad-qag
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qag_jaquad
type: default
args: default
metrics:
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
type: qa_aligned_f1_score_bertscore_question_answer_generation
value: 58.35
- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
type: qa_aligned_recall_bertscore_question_answer_generation
value: 58.38
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
type: qa_aligned_precision_bertscore_question_answer_generation
value: 58.34
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
type: qa_aligned_f1_score_moverscore_question_answer_generation
value: 39.19
- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
type: qa_aligned_recall_moverscore_question_answer_generation
value: 39.17
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
type: qa_aligned_precision_moverscore_question_answer_generation
value: 39.21
---
# Model Card of `lmqg/mt5-small-jaquad-qag`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
- **Language:** ja
- **Training data:** [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="ja", model="lmqg/mt5-small-jaquad-qag")
# model prediction
question_answer_pairs = model.generate_qa("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-jaquad-qag")
output = pipe("ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている6月28日は2人の14回目の結婚記念日であった。")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_jaquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 58.35 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedF1Score (MoverScore) | 39.19 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedPrecision (BERTScore) | 58.34 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedPrecision (MoverScore) | 39.21 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedRecall (BERTScore) | 58.38 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
| QAAlignedRecall (MoverScore) | 39.17 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qag_jaquad
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: None
- model: google/mt5-small
- max_length: 512
- max_length_output: 256
- epoch: 18
- batch: 8
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 8
- label_smoothing: 0.0
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-jaquad-qag/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
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}
}
} | 340 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="quartz14/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"])
```
|
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
}
} | 1,463 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-frozenlake
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="quartz14/taxi-frozenlake", 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"])
```
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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},
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}
}
} | 1,614 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 799.00 +/- 199.62
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Iamvincent -f logs/
python enjoy.py --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 Iamvincent -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 Iamvincent
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 200000),
('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', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"max_length": null
},
"translation_en_to_de": {
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},
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}
}
} | 140 | null | ---
language:
- zh
library_name: transformers
pipeline_tag: text2text-generation
---
```python
from transformers import pipeline
text_generator = pipeline("text-generation", model="svjack/T5-daliy-dialogue")
text_generator(
"你饿吗？"
)
'''
[
{
"generated_text": "是的,我快饿死了"
}
]
'''
``` |
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
]
| null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 9,141 | null | ---
language:
- zh
library_name: transformers
pipeline_tag: text2text-generation
---
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("svjack/T5-dialogue-choose")
model = AutoModelForSeq2SeqLM.from_pretrained("svjack/T5-dialogue-choose")
text = '''
根据如下上下文，选择最优的后续句子
上下文：如何成为程序员？
选项1:多练习做菜。
选项2:了解多门编程语言。
选项3:看几本历史书。
答案：
'''
tokenizer.decode(
model.generate(
tokenizer.encode(
text, return_tensors="pt", add_special_tokens=True
))[0],
skip_special_tokens = True
)
'''
了解多门编程语言。
'''
``` |
DeepPavlov/marianmt-tatoeba-ruen | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
"architectures": [
"MarianMTModel"
],
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}
} | 30 | null | ---
tags:
- autotrain
- text-classification
language:
- zh
widget:
- text: "I love AutoTrain 🤗"
datasets:
- paulkm/autotrain-data-lottery_prod
co2_eq_emissions:
emissions: 11.554897545219454
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2626879382
- CO2 Emissions (in grams): 11.5549
## Validation Metrics
- Loss: 0.146
- Accuracy: 0.960
- Precision: 0.967
- Recall: 0.944
- AUC: 0.986
- F1: 0.955
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/paulkm/autotrain-lottery_prod-2626879382
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("paulkm/autotrain-lottery_prod-2626879382", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("paulkm/autotrain-lottery_prod-2626879382", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
DeepPavlov/roberta-large-winogrande | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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},
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}
}
} | 348 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: carlosmirandad/rl-class-ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeepPavlov/rubert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
"has_space"
]
| feature-extraction | {
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"BertModel"
],
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}
}
} | 17,362 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-Regression-Edmunds_Car_Reviews-all_car_brands
results: []
language:
- en
---
# distilbert-base-uncased-Regression-Edmunds_Car_Reviews-all_car_brands
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2232
- Mse: 0.2232
- Rmse: 0.4724
- Mae: 0.3150
## Model description
This project works to predict the rating of a car based on the review for all automanufacturers.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/NLP%20Regression/Edmunds%20Car%20Reviews%20(All%20Brands)/Edmunds_Consumer_car-Regression-All%20Manufacturers.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/ankkur13/edmundsconsumer-car-ratings-and-reviews
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Rmse | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 0.3936 | 1.0 | 2592 | 0.2282 | 0.2282 | 0.4777 | 0.3158 |
| 0.2163 | 2.0 | 5184 | 0.2160 | 0.2160 | 0.4647 | 0.3106 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |
DeepPavlov/rubert-base-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers",
"has_space"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
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"task_specific_params": {
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},
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}
}
} | 46,991 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: fr
datasets:
- lmqg/qg_frquad
pipeline_tag: text2text-generation
tags:
- answer extraction
widget:
- text: "Pourtant, la strophe spensérienne, utilisée cinq fois avant que ne commence le chœur, constitue en soi un vecteur dont les répétitions structurelles, selon Ricks, relèvent du pur lyrisme tout en constituant une menace potentielle. Après les huit sages pentamètres iambiques, l'alexandrin final <hl> permet une pause <hl>, « véritable illusion d'optique » qu'accentuent les nombreuses expressions archaïsantes telles que did swoon, did seem, did go, did receive, did make, qui doublent le prétérit en un temps composé et paraissent à la fois « très précautionneuses et très peu pressées »."
example_title: "Answering Extraction Example 1"
- text: "Néanmoins, une fois encore, l'arithmétique modulaire est insuffisante pour venir à bout du théorème. Dirichlet utilise de nombreuses techniques analytiques, comme les séries entières et l'analyse complexe. Le fruit de ces travaux donne naissance à une nouvelle branche des mathématiques : la théorie analytique des nombres. L'un des points cruciaux de cette théorie provient de l'unique article de <hl> Bernhard Riemann <hl> en théorie des nombres : Sur le nombre de nombres premiers inférieurs à une taille donnée. Il conjecture une localisation des racines de sa fonction ζ. La recherche de la position des racines, initiée par Dirichlet, devient une préoccupation centrale et reste l'une des conjectures pressenties comme les plus difficiles des mathématiques de notre époque."
example_title: "Answering Extraction Example 2"
model-index:
- name: lmqg/mt5-small-frquad-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_frquad
type: default
args: default
metrics:
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 22.44
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 39.55
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 32.89
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 85.04
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 72.46
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 59.77
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 39.05
---
# Model Card of `lmqg/mt5-small-frquad-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for answer extraction on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
- **Language:** fr
- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="fr", model="lmqg/mt5-small-frquad-ae")
# model prediction
answers = model.generate_a("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-frquad-ae")
output = pipe("Pourtant, la strophe spensérienne, utilisée cinq fois avant que ne commence le chœur, constitue en soi un vecteur dont les répétitions structurelles, selon Ricks, relèvent du pur lyrisme tout en constituant une menace potentielle. Après les huit sages pentamètres iambiques, l'alexandrin final <hl> permet une pause <hl>, « véritable illusion d'optique » qu'accentuent les nombreuses expressions archaïsantes telles que did swoon, did seem, did go, did receive, did make, qui doublent le prétérit en un temps composé et paraissent à la fois « très précautionneuses et très peu pressées ».")
```
## Evaluation
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-frquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_frquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 39.05 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| AnswerF1Score | 59.77 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| BERTScore | 85.04 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_1 | 34.18 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_2 | 29.45 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_3 | 25.72 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| Bleu_4 | 22.44 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| METEOR | 32.89 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| MoverScore | 72.46 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
| ROUGE_L | 39.55 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_frquad
- dataset_name: default
- input_types: ['paragraph_sentence']
- output_types: ['answer']
- prefix_types: None
- model: google/mt5-small
- max_length: 512
- max_length_output: 32
- epoch: 23
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-frquad-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
DeepPavlov/rubert-base-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers",
"has_space"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
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}
} | 148,127 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.71 +/- 19.60
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
]
| text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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"prefix": null
}
}
} | 227 | null | ---
license: creativeml-openrail-m
---
A free iOS and MacOS app for you to generate AI arts on the go: https://apps.apple.com/us/app/%E7%94%BB%E5%83%8F%E7%94%9F%E6%88%90ai-%E3%83%AD%E3%83%BC%E3%82%AB%E3%83%AB-stable-diffusion/id1658459595
This is made possible by using the newest Apple Core ML tool and the pipeline and model files from Hugging Face.
This repository hosts the zipped files for the resources used by the application, for the user to generate the model on iPhone/iPad/or Mac. |
DeepPavlov/xlm-roberta-large-en-ru | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"transformers"
]
| feature-extraction | {
"architectures": [
"XLMRobertaModel"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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}
}
} | 190 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on SetFit/emotion.
It achieves the following results on the evaluation set:
- Loss: 0.2276
- Accuracy: 0.921
- F1: 0.9209
## Model description
This model follows chapter 2 of https://github.com/nlp-with-transformers/notebooks. A few things that were changed from the original notebook:
- the emotion dataset has moved to SetFit/emotion https://github.com/nlp-with-transformers/notebooks/issues/77
- the new dataset doesn't have ClassLabel feature so needed to change int2str method https://github.com/nlp-with-transformers/notebooks/issues/77
- made the label names on inference API human-readable with https://discuss.huggingface.co/t/change-label-names-on-inference-api/3063/3
- function to inspect dataset for existence of certain strings
## Intended uses & limitations
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8732 | 1.0 | 250 | 0.3279 | 0.9055 | 0.9037 |
| 0.259 | 2.0 | 500 | 0.2276 | 0.921 | 0.9209 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
DeltaHub/lora_t5-base_mrpc | [
"pytorch",
"transformers"
]
| null | {
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} | 3 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# 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('MrDivakaruni/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
DemangeJeremy/4-sentiments-with-flaubert | [
"pytorch",
"flaubert",
"text-classification",
"fr",
"transformers",
"sentiments",
"french",
"flaubert-large"
]
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}
} | 226 | null | ---
license: openrail
---
# Oud (عود) Unconditional Diffusion
The Oud is one of the most foundational instruments to all of Arab music. It can be heard in nearly every song, whether the subgenre is rooted in pop or classical music.
Its distinguishing sound can be picked out of a crowd of string instruments with little to no training.
Our Unconditional Diffusion model ensures that we show respect to the sound and culture it has created.
This project could not have been done without [the following audio diffusion tools.](https://github.com/teticio/audio-diffusion)
## Usage
Usage of this model is no different from any other audio diffusion model from HuggingFace.
```python
import torch
from diffusers import DiffusionPipeline
# Setup device and create generator
device = "cuda" if torch.cuda.is_available() else "cpu"
generator = torch.Generator(device=device)
# Instantiate model
model_id = "mijwiz-laboratories/oud_diffusion_unconditional_256"
audio_diffusion = DiffusionPipeline.from_pretrained(model_id).to(device)
# Set seed for generator
seed = generator.seed()
generator.manual_seed(seed)
# Run inference
output = audio_diffusion(generator=generator)
image = output.images[0] # Mel spectrogram generated
audio = output.audios[0, 0] # Playable audio file
```
## Limitations of Model
The dataset used was very small, so the diversity of snippets that can be generated is rather limited. Furthermore, with high intensity segments (think a human playing the instrument with high intensity,)
the realism/naturalness of the generated oud samples degrades. |
Deniskin/essays_small_2000 | []
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} | 0 | null | ---
license: other
---
## / 7th Layer /
<img src="https://i.imgur.com/MjnczlB.png" width="1700" height="">
# (Important Notice:1.6)
default CFG Scale : 7 ±5
default Sampler : DPM++ 2M Karras
default Steps : 25
Negative prompt : (worst quality:1.4), (low quality:1.4) , (monochrome:1.1),
# Don't write a lot of "Negative prompt".
<img src="https://i.imgur.com/tE3PUBi.png" width="480" height="">
## Test Model https://huggingface.co/syaimu/7th_test
<img src="https://i.imgur.com/0xKIUvL.jpg" width="1700" height="">
<img src="https://i.imgur.com/lFZAYVv.jpg" width="1700" height="">
<img src="https://i.imgur.com/4IYqlYq.jpg" width="1700" height="">
<img src="https://i.imgur.com/v2pn57R.jpg" width="1700" height="">
# 7th_anime_v2.5_B → 7th_anime_v2_G
<img src="https://i.imgur.com/K3o28Ci.jpg" width="1700" height="">
<img src="https://i.imgur.com/Bzywbkp.jpg" width="1700" height="">
# other
<img src="https://i.imgur.com/oCZyzdA.jpg" width="1700" height="">
<img src="https://i.imgur.com/sAw842D.jpg" width="1700" height="">
<img src="https://i.imgur.com/lzuYVh0.jpg" width="1700" height="">
<img src="https://i.imgur.com/dOXsoeg.jpg" width="1700" height="">
|
DeskDown/MarianMixFT_en-ms | [
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"text2text-generation",
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} | 5 | null | # Gender-and-Age-Detection <img alt="GitHub" src="https://img.shields.io/github/license/smahesh29/Gender-and-Age-Detection">
<h2>Objective :</h2>
<p>To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture or through webcam.</p>
<h2>About the Project :</h2>
<p>In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. I used the models trained by <a href="https://talhassner.github.io/home/projects/Adience/Adience-data.html">Tal Hassner and Gil Levi</a>. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, I made this a classification problem instead of making it one of regression.</p>
<h2>Dataset :</h2>
<p>For this python project, I had used the Adience dataset; the dataset is available in the public domain and you can find it <a href="https://www.kaggle.com/ttungl/adience-benchmark-gender-and-age-classification">here</a>. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.</p>
<h2>Additional Python Libraries Required :</h2>
<ul>
<li>OpenCV</li>
pip install opencv-python
</ul>
<ul>
<li>argparse</li>
pip install argparse
</ul>
<h2>The contents of this Project :</h2>
<ul>
<li>opencv_face_detector.pbtxt</li>
<li>opencv_face_detector_uint8.pb</li>
<li>age_deploy.prototxt</li>
<li>age_net.caffemodel</li>
<li>gender_deploy.prototxt</li>
<li>gender_net.caffemodel</li>
<li>a few pictures to try the project on</li>
<li>detect.py</li>
</ul>
<p>For face detection, we have a .pb file- this is a protobuf file (protocol buffer); it holds the graph definition and the trained weights of the model. We can use this to run the trained model. And while a .pb file holds the protobuf in binary format, one with the .pbtxt extension holds it in text format. These are TensorFlow files. For age and gender, the .prototxt files describe the network configuration and the .caffemodel file defines the internal states of the parameters of the layers.</p>
<h2>Usage :</h2>
<ul>
<li>Download my Repository</li>
<li>Open your Command Prompt or Terminal and change directory to the folder where all the files are present.</li>
<li><b>Detecting Gender and Age of face in Image</b> Use Command :</li>
python detect.py --image <image_name>
</ul>
<p><b>Note: </b>The Image should be present in same folder where all the files are present</p>
<ul>
<li><b>Detecting Gender and Age of face through webcam</b> Use Command :</li>
python detect.py
</ul>
<ul>
<li>Press <b>Ctrl + C</b> to stop the program execution.</li>
</ul>
# Working:
[](https://youtu.be/ReeccRD21EU)
<h2>Examples :</h2>
<p><b>NOTE:- I downloaded the images from Google,if you have any query or problem i can remove them, i just used it for Educational purpose.</b></p>
>python detect.py --image girl1.jpg
Gender: Female
Age: 25-32 years
<img src="Example/Detecting age and gender girl1.png">
>python detect.py --image girl2.jpg
Gender: Female
Age: 8-12 years
<img src="Example/Detecting age and gender girl2.png">
>python detect.py --image kid1.jpg
Gender: Male
Age: 4-6 years
<img src="Example/Detecting age and gender kid1.png">
>python detect.py --image kid2.jpg
Gender: Female
Age: 4-6 years
<img src="Example/Detecting age and gender kid2.png">
>python detect.py --image man1.jpg
Gender: Male
Age: 38-43 years
<img src="Example/Detecting age and gender man1.png">
>python detect.py --image man2.jpg
Gender: Male
Age: 25-32 years
<img src="Example/Detecting age and gender man2.png">
>python detect.py --image woman1.jpg
Gender: Female
Age: 38-43 years
<img src="Example/Detecting age and gender woman1.png">
# Support :
If you found this project helpful or you learned something from the source code and want to thank me, consider me to pay my internet bills. This would encourage me to create many such projects 👨🏻💻
<ul>
<li><a href="https://www.paypal.me/smahesh29"><b>PayPal</b></a></li>
<li><a href="https://imjo.in/XNZDCJ"><b>₹ (INR)</b></a></li>
<li><b>UPI ID :</b> maheshusa29@oksbi</li>
</ul>
|
DeskDown/MarianMix_en-ja-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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} | 1 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 296.18 +/- 13.50
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DeskDown/MarianMix_en-zh-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
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}
} | 3 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0346
- Rouge1: 16.8527
- Rouge2: 8.331
- Rougel: 16.4475
- Rougelsum: 16.6421
## 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: 5.6e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.7536 | 1.0 | 1209 | 3.2881 | 13.6319 | 5.4635 | 13.0552 | 13.1093 |
| 3.9312 | 2.0 | 2418 | 3.1490 | 16.8402 | 8.3559 | 16.1876 | 16.2869 |
| 3.5987 | 3.0 | 3627 | 3.1043 | 17.9887 | 9.3136 | 17.3034 | 17.4313 |
| 3.4261 | 4.0 | 4836 | 3.0573 | 17.0089 | 8.7389 | 16.5351 | 16.5023 |
| 3.3221 | 5.0 | 6045 | 3.0569 | 16.8461 | 8.0988 | 16.4898 | 16.4927 |
| 3.2549 | 6.0 | 7254 | 3.0511 | 17.3428 | 8.2234 | 16.7312 | 16.8749 |
| 3.2067 | 7.0 | 8463 | 3.0334 | 16.268 | 7.9729 | 15.9342 | 16.0065 |
| 3.1842 | 8.0 | 9672 | 3.0346 | 16.8527 | 8.331 | 16.4475 | 16.6421 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Dhito/am | []
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} | 0 | null | Access to model Faisalrahi/pytorch_model.bin is restricted and you are not in the authorized list. Visit https://huggingface.co/Faisalrahi/pytorch_model.bin to ask for access. |
Dhritam/Zova-bot | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.21 +/- 12.42
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Dhruva/Interstellar | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 739.50 +/- 213.92
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga newbie4000 -f logs/
python enjoy.py --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 newbie4000 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 newbie4000
```
## 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', 2000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Dongmin/testmodel | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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}
} | 11 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0245
- Precision: 0.8602
- Recall: 0.9015
- F1: 0.8804
- Accuracy: 0.9921
## 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
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0157 | 0.06 | 500 | 0.0286 | 0.8569 | 0.8605 | 0.8587 | 0.9908 |
| 0.0156 | 0.11 | 1000 | 0.0287 | 0.8340 | 0.8940 | 0.8630 | 0.9910 |
| 0.0141 | 0.17 | 1500 | 0.0296 | 0.8368 | 0.8853 | 0.8604 | 0.9908 |
| 0.014 | 0.23 | 2000 | 0.0296 | 0.8356 | 0.8915 | 0.8627 | 0.9910 |
| 0.0144 | 0.28 | 2500 | 0.0306 | 0.8310 | 0.8896 | 0.8593 | 0.9906 |
| 0.0136 | 0.34 | 3000 | 0.0290 | 0.8384 | 0.8842 | 0.8607 | 0.9910 |
| 0.0134 | 0.4 | 3500 | 0.0306 | 0.8514 | 0.8779 | 0.8645 | 0.9912 |
| 0.0138 | 0.46 | 4000 | 0.0307 | 0.8475 | 0.8790 | 0.8630 | 0.9910 |
| 0.0139 | 0.51 | 4500 | 0.0301 | 0.8208 | 0.9002 | 0.8587 | 0.9908 |
| 0.014 | 0.57 | 5000 | 0.0320 | 0.8307 | 0.8981 | 0.8631 | 0.9909 |
| 0.0155 | 0.63 | 5500 | 0.0307 | 0.8329 | 0.8992 | 0.8648 | 0.9909 |
| 0.0154 | 0.68 | 6000 | 0.0268 | 0.8403 | 0.8971 | 0.8677 | 0.9913 |
| 0.0178 | 0.74 | 6500 | 0.0269 | 0.8548 | 0.8869 | 0.8705 | 0.9916 |
| 0.0177 | 0.8 | 7000 | 0.0268 | 0.8552 | 0.8904 | 0.8725 | 0.9917 |
| 0.0178 | 0.85 | 7500 | 0.0267 | 0.8498 | 0.8972 | 0.8729 | 0.9917 |
| 0.017 | 0.91 | 8000 | 0.0259 | 0.8517 | 0.8969 | 0.8737 | 0.9917 |
| 0.0176 | 0.97 | 8500 | 0.0249 | 0.8523 | 0.8921 | 0.8717 | 0.9916 |
| 0.0157 | 1.02 | 9000 | 0.0274 | 0.8535 | 0.8990 | 0.8757 | 0.9918 |
| 0.0134 | 1.08 | 9500 | 0.0293 | 0.8375 | 0.9060 | 0.8704 | 0.9913 |
| 0.0132 | 1.14 | 10000 | 0.0278 | 0.8648 | 0.8864 | 0.8755 | 0.9919 |
| 0.0135 | 1.19 | 10500 | 0.0273 | 0.8540 | 0.8958 | 0.8744 | 0.9917 |
| 0.0133 | 1.25 | 11000 | 0.0277 | 0.8442 | 0.9034 | 0.8728 | 0.9917 |
| 0.0138 | 1.31 | 11500 | 0.0276 | 0.8484 | 0.9035 | 0.8751 | 0.9917 |
| 0.0141 | 1.37 | 12000 | 0.0274 | 0.8501 | 0.9016 | 0.8751 | 0.9918 |
| 0.0137 | 1.42 | 12500 | 0.0274 | 0.8529 | 0.9010 | 0.8763 | 0.9918 |
| 0.014 | 1.48 | 13000 | 0.0269 | 0.8509 | 0.9022 | 0.8758 | 0.9919 |
| 0.0141 | 1.54 | 13500 | 0.0260 | 0.8653 | 0.8926 | 0.8787 | 0.9920 |
| 0.0149 | 1.59 | 14000 | 0.0258 | 0.8521 | 0.9048 | 0.8777 | 0.9919 |
| 0.0149 | 1.65 | 14500 | 0.0257 | 0.8607 | 0.8980 | 0.8790 | 0.9921 |
| 0.0152 | 1.71 | 15000 | 0.0257 | 0.8596 | 0.9001 | 0.8794 | 0.9920 |
| 0.015 | 1.76 | 15500 | 0.0257 | 0.8556 | 0.9032 | 0.8788 | 0.9920 |
| 0.0157 | 1.82 | 16000 | 0.0248 | 0.8620 | 0.8993 | 0.8802 | 0.9922 |
| 0.0158 | 1.88 | 16500 | 0.0251 | 0.8573 | 0.9036 | 0.8798 | 0.9921 |
| 0.0163 | 1.93 | 17000 | 0.0248 | 0.8579 | 0.9034 | 0.8800 | 0.9921 |
| 0.017 | 1.99 | 17500 | 0.0245 | 0.8602 | 0.9015 | 0.8804 | 0.9921 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0
- Datasets 2.3.2
- Tokenizers 0.10.3
|
Doogie/Waynehills-KE-T5-doogie | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 160.35 +/- 100.26
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Waynehillsdev/Waynehills-STT-doogie-server | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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} | 61 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="PakanunNoa/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"])
```
|
Doohae/q_encoder | [
"pytorch"
]
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} | 3 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.94 +/- 22.10
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Doxophobia/DialoGPT-medium-celeste | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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}
} | 11 | null | ---
license: apache-2.0
---
## 简介 Brief Introduction
1.1亿参数的ernie-1.0模型
## 模型信息 Released Model Information
当前发布的pytorch版本ERNIE-base，是从huggingface下载并修复vocab和config对不上的问题。
This released pytorch model is downloading from huggingface, then fixed the vocab and config not matching issue.
## 使用 Usage
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
tokenizer=AutoTokenizer.from_pretrained("xiaoqin/ernie-1.0-base-zh")
model=AutoModelForMaskedLM.from_pretrained("xiaoqin/ernie-1.0-base-zh")
```
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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"model_type": "bert",
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}
} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the timit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4243
- Wer: 0.2830
## 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: 32
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5112 | 3.45 | 500 | 1.1699 | 0.8236 |
| 0.5349 | 6.9 | 1000 | 0.3911 | 0.3609 |
| 0.1875 | 10.34 | 1500 | 0.3993 | 0.3170 |
| 0.1113 | 13.79 | 2000 | 0.3870 | 0.3046 |
| 0.0778 | 17.24 | 2500 | 0.4056 | 0.2963 |
| 0.0561 | 20.69 | 3000 | 0.3781 | 0.2918 |
| 0.0461 | 24.14 | 3500 | 0.4186 | 0.2857 |
| 0.0375 | 27.59 | 4000 | 0.4243 | 0.2830 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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} | 30 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 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": 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": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
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} | 2,973 | 2022-12-27T10:14:22Z | ---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
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
...
```
|
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
} | 11,644 | 2022-12-27T10:20:45Z | ---
license: mit
---
## Sentiment Analysis API
This is the deployment part of the project.
### Training:
Run the Google Colab notebook (Runtime = "GPU") (https://colab.research.google.com/drive/1EuF5FDl1X8VnuOO5RxzmM0c9TbtQrVm9?usp=sharing)
### Fine Tuning
1) Increasing #epochs
2) Increasing BATCH_SIZE to 32
3) Changing Adam optimizer rate
### Usage
1) Clone te repository
2) Set up the conda environment with requirements.txt
3) In terminal, run the command- uvivorn sentiment_analyzer.api:app --reload |
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
} | 8,621,271 | 2022-12-27T10:22:47Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: mitro99/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 3,377,486 | 2022-12-27T10:29:09Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="m00ra/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"])
```
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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},
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},
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}
}
} | 1,814 | null | Access to model S-Sajad-Hosseini/q-Taxi-v3 is restricted and you are not in the authorized list. Visit https://huggingface.co/S-Sajad-Hosseini/q-Taxi-v3 to ask for access. |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
} | 328,585 | 2022-12-27T10:43:25Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="m00ra/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"])
```
|
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
} | 8,214 | 2022-12-27T10:46:48Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 0.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="S-Sajad-Hosseini/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
1n3skh/idk | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 714.50 +/- 219.04
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lotek93 -f logs/
python enjoy.py --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 lotek93 -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 lotek93
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('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', 20000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Ab2021/bookst5 | []
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} | 0 | 2022-12-27T20:56:20Z | ---
tags:
- conversational
---
# Black Doom DialoGPT Model |
AdapterHub/bert-base-uncased-pf-comqa | [
"bert",
"en",
"dataset:com_qa",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
]
| question-answering | {
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}
} | 0 | null | ---
language:
- unk
tags:
- autotrain
- summarization
datasets:
- ell-hol/autotrain-data-test-orangesum
widget:
- text: I love AutoTrain 🤗
co2_eq_emissions:
emissions: 675.7789931017469
model-index:
- name: ell-hol/mT5-OrangeSum
results:
- task:
type: summarization
name: Summarization
dataset:
name: orange_sum
type: orange_sum
config: abstract
split: validation
metrics:
- type: rouge
value: 33.377
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhjMWIxYmNmNDYzNTMzMDM2YjQyOTdkYjYyMDJkZDhlNzQ2ZDVkNGM2YTIzODU4ZWYwZDg2ODZkN2U5OTk2MSIsInZlcnNpb24iOjF9.UL_nv_GGJ75LMgDmRjvrp0dYhCyjz-h5txS1ljDFS7k9Yy6iJ0QnTebou1tsLFtj7sBSvUKvZeyqFXEHN7SBCg
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value: 14.4472
name: ROUGE-2
verified: true
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- type: rouge
value: 24.1902
name: ROUGE-L
verified: true
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- type: rouge
value: 25.5277
name: ROUGE-LSUM
verified: true
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- type: loss
value: 1.6347737312316895
name: loss
verified: true
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value: 48.4967
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzk3YjMxZWY2NzE5ZWMxZjBhYmE5YzU2YTM3MzNmMjlmNmJjM2MyMzY4ZTE1MjI1ZTNkN2YxOWZhOThmYzljMyIsInZlcnNpb24iOjF9._I_I9B66dT3S8RMMmMACG3YjIQYcXzmodriDWM33jRa4X6NFQx0b6_YHNP7K-uLEm8qD31bgb0NlsaRA37qLBA
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2638979565
- CO2 Emissions (in grams): 675.7790
## Validation Metrics
- Loss: 1.631
- Rouge1: 33.348
- Rouge2: 14.481
- RougeL: 24.210
- RougeLsum: 25.514
- Gen Len: 48.497
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ell-hol/autotrain-test-orangesum-2638979565
``` |
AdapterHub/roberta-base-pf-conll2000 | [
"roberta",
"en",
"dataset:conll2000",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:chunk/conll2000"
]
| token-classification | {
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"model_type": "roberta",
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}
} | 3 | null | ---
language:
- "zh"
tags:
- "chinese"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
---
# deberta-base-chinese-ud-goeswith
## Model Description
This is a DeBERTa(V2) model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-base-chinese-upos](https://huggingface.co/KoichiYasuoka/deberta-base-chinese-upos).
## How to Use
```py
class UDgoeswith(object):
def __init__(self,bert):
from transformers import AutoTokenizer,AutoModelForTokenClassification
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForTokenClassification.from_pretrained(bert)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=self.tokenizer(text,return_offsets_mapping=True)
v=w["input_ids"]
x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
with torch.no_grad():
e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
g=self.model.config.label2id["X|_|goeswith"]
r=numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
p=numpy.zeros(m.shape)
p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
for i in range(1,m.shape[0]):
m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text+"\n"
v=[(s,e) for s,e in w["offset_mapping"] if s<e]
for i,(s,e) in enumerate(v,1):
q=self.model.config.id2label[p[i,h[i]]].split("|")
u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=UDgoeswith("KoichiYasuoka/deberta-base-chinese-ud-goeswith")
print(nlp("我把这本书看完了"))
```
with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-chinese-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("我把这本书看完了"))
```
|
Aimendo/Triage | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: nemanjar/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ajteks/Chatbot | []
| null | {
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}
} | 0 | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -55.72 +/- 62.76
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 5000000
'learning_rate': 0.0001
'num_envs': 16
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'alvarobb/ppo-LunarLander-v2'
'batch_size': 2048
'minibatch_size': 512}
```
|
Akaramhuggingface/News | []
| null | {
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}
}
} | 0 | null | ---
language:
- en
tags:
- stable-diffusion
- text-to-image
- image-to-image
- diffusers
license: creativeml-openrail-m
inference: true
---
**Big update DucHaitenAIart_v3.1**
*Big update of DucHaitenAIart, v3.1 is able to receive more diverse, more detailed prompts with gorgeous colors and more realistic shadows. The image has the breath of 3D anime, but the material is much more realistic. The weak point is that some celebrity images are no longer in the model, a bit too 3d anime might make some people dislike, the image of the teeth is a bit lacking in detail.
**Please support me by becoming a patron:**
https://www.patreon.com/duchaitenreal
*****
All sample images only use text to image, no editing, no image to image, no restore face no highres fix no extras.
*****
Hello, sorry for my lousy english.
After days of trying and retrying hundreds of times, with dozens of different versions, DucHaitenAIart finally released the official version.
Improved image sharpness, more realistic lighting correction, more shooting angles, the only downside is that it's less flexible and less random than beta-v6.0, so I'm still will leave beta-v6.0 for anyone to download.
This model can create NSFW images but since it is not a hentai and porn model, anything really hardcore will be difficult to create. But, To make the model work better with NSFW images, add “hentai, porn, rule 34” to the prompt
Always add to the prompt “masterpiece, best quality, 1girl or 1boy, realistic, anime or cartoon (it's two different styles, but I personally prefer anime), 3D, pixar, (add “pin-up”) ” if you are going to give your character a sexy pose), highly detail eyes, perfect eyes, both eyes are the same, (if you don't want to draw eyes, don't add them), smooth, perfect face, hd, 2k, 4k , 8k, 16k
Add to the prompt: “extremely detailed 8K, high resolution, ultra quality” to further enhance the image quality, but it may weaken the AI's interest in other keywords.
You can add “glare, Iridescent, Global illumination, real hair movement, realistic light, realistic shadow” to the prompt to create a better lighting effect, but the image will then become too realistic, if you don't want to. Please adjust it accordingly.
*****
Sampler: DPM++ 2S a Karras
+ negative prompt:
illustration, painting, cartoons, sketch, (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad hands, ((monochrome)), ((grayscale)), collapsed eyeshadow, multiple eyeblows, vaginas in breasts, (cropped), oversaturated, extra limb, missing limbs, deformed hands, long neck, long body, imperfect, (bad hands), signature, watermark, username, artist name, conjoined fingers, deformed fingers, ugly eyes, imperfect eyes, skewed eyes, unnatural face, unnatural body, error
*****
Some test:
|
Akari/albert-base-v2-finetuned-squad | [
"pytorch",
"tensorboard",
"albert",
"question-answering",
"dataset:squad_v2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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}
}
} | 13 | 2022-12-28T10:38:19Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 374.00 +/- 214.89
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** 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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga PabloTa -f logs/
python enjoy.py --algo qrdqn --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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga PabloTa -f logs/
rl_zoo3 enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga PabloTa
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 25000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.225),
('frame_stack', 3),
('learning_rate', 0.023),
('n_timesteps', 1000000.0),
('normalize', False),
('optimize_memory_usage', False),
('policy', 'CnnPolicy')])
```
|
Akash7897/bert-base-cased-wikitext2 | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
} | 8 | null | # **roberta-base model is fine tuned on Airline Passenger Complaint Dataset.**
- model can be used to determine the sentiment of the statement
|
Akash7897/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
} | 31 | 2022-12-28T10:42:35Z | ---
inference: true
language:
- en
tags:
- stable-diffusion
- text-to-image
- embedding
license: wtfpl
---
# rr_inferno embedding - SD 2.1
## Exemple of Prompt
"Portrait of a rr_inferno skull skeleton, made of lava, fire, giant, concept art, splash art, global lightning, artwork of a phoenix, nine tails, fiery, gorgeous digital painting"
## Training
Trained for 850 steps. 20 images, 6 vectors. Batch size of 3, 3 grad acc steps, learning rate of 0.0004:200, 0.0002:400, 0.0001:1000,0.00005
|
Akashpb13/Swahili_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sw",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
} | 10 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### rbto3v2 Dreambooth model trained by rudzinskimaciej with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
|
Akashpb13/xlsr_kurmanji_kurdish | [
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kmr",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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} | 10 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ell-hol/autotrain-data-mt5-dialogsum
co2_eq_emissions:
emissions: 248.06396898781733
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2644579647
- CO2 Emissions (in grams): 248.0640
## Validation Metrics
- Loss: 1.316
- Rouge1: 40.914
- Rouge2: 16.140
- RougeL: 33.122
- RougeLsum: 35.661
- Gen Len: 34.075
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ell-hol/autotrain-mt5-dialogsum-2644579647
``` |
AkshaySg/GrammarCorrection | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 274.55 +/- 9.76
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AkshaySg/LanguageIdentification | [
"multilingual",
"dataset:VoxLingua107",
"LID",
"spoken language recognition",
"license:apache-2.0"
]
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
widget:
- text: a pkblz ball in the middle of a miniature jungle
- text: a photo of a spectral ornate pkblz ball, trending on artstation, realistic
- text: a colored pencil sketch of a pkblz ball
---
# The Pokeball Machine
The **Pokeball Machine** is a Dreambooth model for the `pokeball` concept (represented by the `pkblz` identifier).
It applies to the *wildcard* theme.
It is fine-tuned from `CompVis/stable-diffusion-v1-4` checkpoint on a small dataset of pokeball images (i.e., images of the red-white original pokeball).
It can be used by modifying the `instance_prompt`: **a pkblz ball in the middle of a miniature jungle**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
#### Fine-Tuning Details
- Number of training images: 31
- Learning rate: 2e-06
- Training steps: 800
- Guidance Scale: 10
- Inference Steps: 50-75
#### Output Examples
<table>
<tr>
<td>a blueprint photo of a <b>pkblz</b> ball</td>
<td>a photo of a cybernetic <b>pkblz</b> ball, wide shot</td>
<td>a photo of a <b>pkblz</b> ball in the style vintage disney</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(1).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(2).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(3).png" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a mosaic <b>pkblz</b> ball lying in an antique temple</td>
<td>a photo of a detailed ornate <b>pkblz</b> ball</td>
<td>a pkblz ball underwater</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(4).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(5).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(6).png" style="height:200px"> </td>
</tr>
<tr>
<td>a <b>pkblz</b> ball in the middle of a miniature jungle</td>
<td>a <b>pkblz</b> ball underwater</td>
<td>a mystic <b>pkblz</b> ball, trending on artstation</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(7).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(8).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(9).png" style="height:200px"> </td>
</tr>
<tr>
<td>a <b>pkblz</b> ball underwater, trending on artstation</td>
<td>a wooden <b>pkblz</b> ball</td>
<td>a <b>pkblz</b> ball hovering over a pond</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(10).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(11).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(12).png" style="height:200px"> </td>
</tr>
<tr>
<td>a <b>pkblz</b> ball on a sunny tropical beach</td>
<td>a steampunk <b>pkblz</b> ball, trending on artstation</td>
<td>a colored pencil sketch of a <b>pkblz</b> ball</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(13).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(14).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(15).png" style="height:200px"> </td>
</tr>
<tr>
<td>a photo of a spectral ornate <b>pkblz</b> ball, trending on artstation, realistic</td>
<td>a sunset photo of a <b>pkblz</b> ball</td>
<td>a watercolor photo of a <b>pkblz</b> ball</td>
</tr>
<tr>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(16).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(17).png" style="height:200px"> </td>
<td align="center"><img src="https://huggingface.co/simonschoe/pokeball-machine/resolve/main/output/pokeball%20(18).png" style="height:200px"> </td>
</tr>
</table>
## Usage
```python
from diffusers import StableDiffusionPipeline
import torch
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pipeline = StableDiffusionPipeline.from_pretrained('simonschoe/pokeball-machine').to(device)
prompt = "a pkblz ball in the middle of a miniature jungle"
image = pipeline(
prompt,
num_inference_steps=50,
guidance_scale=10,
num_images_per_prompt=1
).images[0]
image
```
|
Aleksandar/electra-srb-oscar | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9458064516129032
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2699
- Accuracy: 0.9458
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2203 | 1.0 | 318 | 3.1656 | 0.7532 |
| 2.4201 | 2.0 | 636 | 1.5891 | 0.8558 |
| 1.1961 | 3.0 | 954 | 0.8037 | 0.9152 |
| 0.5996 | 4.0 | 1272 | 0.4888 | 0.9326 |
| 0.3306 | 5.0 | 1590 | 0.3589 | 0.9439 |
| 0.2079 | 6.0 | 1908 | 0.3070 | 0.9439 |
| 0.1458 | 7.0 | 2226 | 0.2809 | 0.9458 |
| 0.1155 | 8.0 | 2544 | 0.2740 | 0.9461 |
| 0.1021 | 9.0 | 2862 | 0.2699 | 0.9458 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0+cu116
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Ana1315/ana | []
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} | 0 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# 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('cm2300/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
AnonARR/qqp-bert | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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}
} | 38 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### wolde_eyu_5120512 Dreambooth model trained by Eyuel with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
AnonymousSub/AR_EManuals-BERT | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.80 +/- 23.52
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/AR_cline | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
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:
- Loss: 0.2208
- Accuracy: 0.9205
- F1: 0.9207
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8134 | 1.0 | 250 | 0.3103 | 0.9055 | 0.9035 |
| 0.2419 | 2.0 | 500 | 0.2208 | 0.9205 | 0.9207 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0
|
AnonymousSub/AR_rule_based_roberta_only_classfn_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- food
widget:
- text: a photo of jairzza pizza in the Acropolis
---
# DreamBooth model for the jairzza concept trained by jairNeto on the jairNeto/pizza dataset.
This is a Stable Diffusion model fine-tuned on the jairzza concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of jairzza pizza**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `pizza` images for the food theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('jairNeto/jairzza-pizza')
image = pipeline().images[0]
image
```
|
AnonymousSub/AR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 4 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 767.00 +/- 226.41
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga toastedshibe -f logs/
python enjoy.py --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 toastedshibe -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 toastedshibe
```
## 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)])
```
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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"RobertaModel"
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}
} | 6 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1841
- F1: 0.8550
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2971 | 1.0 | 1252 | 0.1979 | 0.8076 |
| 0.1562 | 2.0 | 2504 | 0.1828 | 0.8404 |
| 0.101 | 3.0 | 3756 | 0.1841 | 0.8550 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/AR_rule_based_roberta_twostagetriplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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} | 2 | 2022-12-29T00:12:23Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 302.91 +/- 10.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/AR_rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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"BertModel"
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}
} | 1 | 2022-12-29T00:25:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-es
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8937
- Rouge1: 32.6939
- Rouge2: 11.794
- Rougel: 31.9982
- Rougelsum: 31.9902
- Gen Len: 15.7947
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.251 | 1.0 | 7061 | 1.8937 | 32.6939 | 11.794 | 31.9982 | 31.9902 | 15.7947 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/AR_rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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"architectures": [
"BertModel"
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}
} | 2 | 2022-12-29T00:35:01Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -297.00 +/- 106.21
name: mean_reward
verified: false
---
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/AR_rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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"BertModel"
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}
} | 6 | 2022-12-29T00:43:36Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# 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('renee127/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
AnonymousSub/AR_specter | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
],
"model_type": "bert",
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}
} | 2 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 701.50 +/- 210.88
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga matthh -f logs/
python enjoy.py --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 matthh -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 matthh
```
## 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)])
```
|
AnonymousSub/EManuals_BERT_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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"BertModel"
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} | 2 | 2022-12-29T01:05:43Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- wildcard
widget:
- text: a photo of ramrick character as a pickle
---
# DreamBooth model for the ramrick concept trained by Kayvane on the Kayvane/dreambooth-hackathon-rick-and-morty-images-square dataset.
Notes:
- trained on square images, 20k steps on google colab
- character name is ramrick, many pictures get blocked as nsfw - possibly because the subtoken #ick is close to something else
- model is trained for too many steps / overfitted as it is effectively recreating the input images
This is a Stable Diffusion model fine-tuned on the ramrick concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of ramrick character**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `character` images for the wildcard theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Kayvane/rick-and-morty-ramrick-character')
image = pipeline().images[0]
image
```
|
AnonymousSub/SR_EManuals-BERT | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"BertModel"
],
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} | 6 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# 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('renee127/sd-class-butterflies-64')
image = pipeline().images[0]
image
```
|
AnonymousSub/SR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
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}
} | 1 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.03 +/- 22.81
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/SR_SDR_HF_model_base | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 1 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **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: thiagoms7/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/SR_bert-base-uncased | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: t5-small-finetuned-en-to-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-es
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8937
- Bleu: 7.4133
- Gen Len: 15.9653
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 2.27 | 1.0 | 7061 | 1.8937 | 7.4133 | 15.9653 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 1 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: 001_M-BERT-claim-classifier
results: []
---
<!-- 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. -->
# この「クレーム判別」モデルの使い方
このモデルは、当該クレームがどのプロダクトについてのものかを判別します。右側の"Hosted inference API" 直下のBOXにお好きなクレームを入力し、"Compute"ボタンをクリックして下さい。30〜60秒前後で答えが返ってきます。答えは以下の５つのlabelのいずれかです。
入力は最大300〜400文字、日本語・英語他100カ国語以上に対応してます。教育用ですので、機密情報は入力しないで下さい。
# 5つのlabelの定義:
- LABEL_0 : Bank account or service (銀行口座・サービス)
- LABEL_1 : Checking or savings account（当座預金・普通預金）
- LABEL_2 : Consumer Loan（消費者ローン）
- LABEL_3 : Credit card（クレジットカード）
- LABEL_4 : Mortgage（住宅ローン）
### 入力例 （正解 4: 'Mortgage'）
「私の夫と私はローンデポを通じてローンのRefiローンを申請し、その後承認されました。私たちはそれが次週になると言われているプロセスで前進することにしました。 両当事者と鑑定評価で実行されました。私たちの料金は次の週に有効であることが開示されました。貸出金の担当者によって連絡しました。ローンが45日以内に閉じなかった場合、当社の元のレートオファーが拡張されます。ロックレートが期限切れになったことを示すXXXXから別のコミュニケーション（Eメール）を受け取り、新しい料金を取得する必要があります。」
# How to use this "claim classification" model
This model determines which product the claim is about. Enter your favorite claim in the box directly under "Hosted inference API" on the right, and click the "Compute" button. You will receive an answer within 30-60 seconds. The answer is one of the following five labels. You can enter a maximum of 400 to 500 characters, and it supports Japanese, English, and more than 100 languages.
# Warning
This is for educational purposes only, please do not enter confidential information.
# Definition of 5 labels
- LABEL_0 : Bank account or service
- LABEL_1 : Checking or savings account
- LABEL_2 : Consumer Loan
- LABEL_3 : Credit card
- LABEL_4 : Mortgage
# Input example (correct answer 4: 'Mortgage')
I drew an advance of $2900.00 from my HELOC that I have with Wells Fargo. I have auto pay with Wells Fargo and a scheduled payment of $100.00 was taken from mychecking ac. I entered the bank, CA branch and paid $2800.00 to pay off the advance. Since that time, various transactions have been posted to this account. There is a principal adj debit in the amount of $170.00 followed by a principal reversal in the amount of $91.
# 001_M-BERT-claim-classifier
It achieves the following results on the evaluation set:
## Model description
bert-base-multilingual-cased
## Intended uses & limitations
This is solely for educational purposes. This cannot be used for investments or businesses in practice. I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on me to correct any errors or defects in the codes and the software.
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_only_classfn_epochs_1_shard_1 | [
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"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
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:
- Loss: 0.2139
- Accuracy: 0.9285
- F1: 0.9285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8138 | 1.0 | 250 | 0.3186 | 0.9005 | 0.8957 |
| 0.2426 | 2.0 | 500 | 0.2139 | 0.9285 | 0.9285 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-es
split: train
args: en-es
metrics:
- name: Bleu
type: bleu
value: 0.5036
---
<!-- 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. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4340
- Bleu: 0.5036
- Gen Len: 18.1951
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 2.7976 | 1.0 | 4674 | 2.4859 | 0.4505 | 18.203 |
| 2.7472 | 2.0 | 9348 | 2.4340 | 0.5036 | 18.1951 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 2 | null | Access to model Ralf-ca/my-sentiment-model is restricted and you are not in the authorized list. Visit https://huggingface.co/Ralf-ca/my-sentiment-model to ask for access. |
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 4 | null | ---
license: cc-by-nc-sa-4.0
language:
- en
thumbnail: "https://huggingface.co/GeneralAwareness/VintagePhotos/resolve/main/00122-2365281862-color%20photo%20emma%20stone%20in%20the%20style%20of%20Vint.png"
tags:
- stable-diffusion
- v2
- text-to-image
- image-to-image
- Embedding
---
Textual Inversion Embedding by General Awareness For SD 2.x trained on 768x768 images from various sources.
Install by downloading the .pt embedding, and put it in the \embeddings folder.
The two embeddings are a one two punch as Vint-3000 is more 1880s style of photography (some seeds will be different) while the Vint is more for 1940s onward though both can be used for anything you can dream of.
Use keyword: vint, or vint-3000 depending on the embedding, and effect you are trying to achieve.
color photo morgan freeman in the style of Vint-3000
color photo morgan freeman in the style of Vint
color photo emma stone in the style of Vint
color photo emma stone in the style of Vint-3000
color photo post apocalyptic city in the style of Vint-3000
color photo post apocalyptic city in the style of Vint
 |
AnonymousSub/SR_rule_based_roberta_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 8 | null | ---
license: apache-2.0
---
# Erlangshen-BERT-120M-IE-Chinese
* Github: [GTS-Engine](https://github.com/IDEA-CCNL/GTS-Engine)
* Documentation: [GTS-Engine](https://gts-engine-doc.readthedocs.io/en/latest/docs/quick_start.html)
## 简介 Brief Introduction
本模型基于大规模信息抽取数据进行预训练，可支持few-shot、zero-shot场景下的实体识别、关系三元组抽取任务。
This model is pre-trained on large-scale information extraction data, to better support Named Entity Recognition (NER) and Relation Extraction (RE) tasks in few-shot/zero-shot scenarios.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| ---------- | ---------- | -------------- | -------- | ------------ | -------- |
| 通用 General | 信息抽取 Information Extraction | 二郎神 Erlangshen | BagualuIEModel | 120M | Chinese |
## 下游效果 Performance
Erlangshen-BERT-120M-IE-Chinese在多个信息抽取任务下进行测试。
其中，zh_weibo/MSRA/OntoNote4/Resume为NER任务，其中MSRA在原始数据下进行测试；SanWen/FinRE作为实体关系联合抽取任务进行测试，非单一关系分类任务。
部分参数设置如下：
```
batch_size=16
precision=16
max_epoch=50
lr=2e-5
weight_decay=0.1
warmup=0.06
max_length=512
```
我们分别在随机种子123/456/789下进行测试，并以[MacBERT-base, Chinese](https://github.com/ymcui/MacBERT)作为预训练模型保持相同参数进行训练作为对比baseline，得到效果计算平均，效果如下：
| Dataset | Training epochs | Test precision | Test recall | Test f1 | Baseline f1 |
| --------- | --------------- | -------------- | ----------- | ------- | ----------- |
| zh_weibo | 10.3 | 0.7282 | 0.6447 | 0.6839 | 0.6778 |
| MSRA | 5 | 0.9374 | 0.9299 | 0.9336 | 0.8483 |
| OntoNote4 | 9 | 0.8640 | 0.8634 | 0.8636 | 0.7996 |
| Resume | 15 | 0.9568 | 0.9658 | 0.9613 | 0.9479 |
| SanWen | 6.7 | 0.3655 | 0.2072 | 0.2639 | 0.2655 |
| FinRE | 7 | 0.5190 | 0.4274 | 0.4685 | 0.4559 |
## 使用 Usage
GTS引擎（GTS-Engine）是一款开箱即用且性能强大的自然语言理解引擎，能够仅用小样本就能自动化生产NLP模型。GTS Engine包含两个训练引擎：乾坤鼎和八卦炉。
本模型为可在GTS-Engine八卦炉引擎信息抽取任务中，作为预训练模型进行finetune。
GTS-Engine文档参考：[GTS-Engine](https://gts-engine-doc.readthedocs.io/en/latest/docs/about.html)
## 引用
如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/GTS-Engine):
You can also cite our website:
```
@misc{GTS-Engine,
title={GTS-Engine},
author={IDEA-CCNL},
year={2022},
howpublished={\url{https://github.com/IDEA-CCNL/GTS-Engine}},
}
```
|
AnonymousSub/bert_mean_diff_epochs_1_shard_10 | [
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} | 4 | null | ---
language: en
license: mit
tags:
- exbert
datasets:
- squad_v2
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
model-index:
- name: Shobhank-iiitdwd/DistBERT-squad2-QA-768d
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type: exact_match
value: 73.8248
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFmZmFiN2E5ODZkOTkyMjQ1NTUzMmQwMjc0M2RlYzVlNmM4YTFlNzA4YzIwY2JkY2EyNDg2ZTY3OTdjZTVlZiIsInZlcnNpb24iOjF9.ZZ6c2OI3lzeNhuSWTh28j00zk-sPrqkTvdVBZv2wJc1D4YnR-xOj72haybT6MV_xeYqTg3-x9L8PsWSS20NaDw
- type: f1
value: 77.1684
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzAxMDk1YzI5ZjA2N2ZmMzAxNjgxYzJiNzAzYmI1ZWU5ZDRmYWY3OWJmMjlmNDcyMGE0YWY5NjNhZTk4YWY5ZSIsInZlcnNpb24iOjF9.rF3raNGUSYv5D2xzWLZztD99vwDKvWb22LG32RomrDGP6XKTbCVqZzAw5UFw93jKb0VoLApbQQ-AOGxLj3U_Cg
---
## Overview
**Language model:** Shobhank-iiitdwd/DistBERT-squad2-QA
**Language:** English
**Training data:** SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation
**Eval data:** SQuAD 2.0 dev set
**Infrastructure**: 1x V100 GPU
**Published**: Dec 8th, 2021
## Details
- haystack's intermediate layer and prediction layer distillation features were used for training. bert-base-uncased-squad2 was used as the teacher model and DBERT_General_6L_768D was used as the student model.
## Hyperparameters
### Intermediate layer distillation
```
batch_size = 26
n_epochs = 5
max_seq_len = 384
learning_rate = 5e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 1
```
### Prediction layer distillation
```
batch_size = 26
n_epochs = 5
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 1
distillation_loss_weight = 1.0
```
## Performance
```
"exact": 71.87736882001179
"f1": 76.36111895973675
```
|
AnonymousSub/consert-techqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"BertForQuestionAnswering"
],
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}
} | 4 | null | ---
license: other
---
model A(*0.48) + model B(*0.32) + model C(*0.20)<br>
anime model<br>
A detailed bird's-eye view of the city, a variety of poses with such a detailed background and clothing patterns, angle variations, underwear in every position on the bed, nudity, and a custom model that is too perfect to do anything!<br>
<br>sample1.png<br>
size:1280*768 Step:50 CfgScale:7 DPM++2M highres.fix:OFF<br>
<br>
recommend prompt add 1 (extremely detailed CG unity 8k wallpaper)<br>
recommend prompt add 2 (sparkle, light rays, lens flare, light particles)<br>
recommend prompt add 3 (hyper detailed, exquisite detail)<br>
recommend prompt add 4 (cinematic lighting, sharp focus, bokeh highlight, cinematic postprocessing)<br>
highres.fix<br>
OFF:Increase the priority of landscape rendering , auto wide angle<br>
ON:Prioritize portraiture<br>
recommended to use WaifuDiffusion v1.3 VAE<br>
2023.1.24<br>
v2.0(=v1.2) = v1.0(*0.80) + model D(*0.20)<br>
:improvement of facial expressions<br>
:Improved instability of mouth rendering<br>
:Improved weak eye highlights<br>
:Enhance the beauty of the depiction of all parts of a woman<br>
:rich composition<br>
:It is recommended to set hires.fix=ON for both background rendering and portrait rendering.<br>
:Depiction of limbs may be slightly unstable<br>
:Beautiful indoor background that looks like a real photo<br>
<br>sample2.png<br>
size:640*384->1280*768 Step:50 CfgScale:7 DPM++2M hires.fix:ON Latent upscale:*2 step:50 strength:0.75<br>
<br>
prohibit commercial use<br>
|
AnonymousSub/declutr-emanuals-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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"RobertaForQuestionAnswering"
],
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} | 4 | null | ---
license: apache-2.0
language:
- en
- zh
datasets:
- mnist
metrics:
- accuracy
tags:
- classification
---
MNIST
======
## Intro
hand-written digital recognition
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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"RobertaModel"
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}
} | 2 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="orenk/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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}
} | 7 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.63 +/- 31.51
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from huggingface_sb3 import load_from_hub
checkpoint = load_from_hub(
repo_id="ali-issa/ppo-lunarLander-v2",
filename="Landing_lunar_AI.zip",
)
...
```
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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"BertModel"
],
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}
} | 10 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 272.32 +/- 15.23
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_twostagetriplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
} | 27 | null | ---
tags:
- generated_from_trainer
model-index:
- name: test_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9501
- eval_accuracy: 0.491
- eval_runtime: 64.4098
- eval_samples_per_second: 31.051
- eval_steps_per_second: 3.881
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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}
} | 5 | null | ---
language:
- hi
metrics:
- wer
tags:
- ASR
- Speech Recognition
- Hindi
--- |
Anthos23/my-awesome-model | [
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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},
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}
} | 30 | 2022-12-29T13:10:57Z | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
- text-to-audio
language:
- en
--- |
Anthos23/sentiment-roberta-large-english-finetuned-sentiment-analysis | []
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}
} | 0 | 2022-12-29T13:12:24Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3_0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="CyantifiCQ/Taxi-v3_0", 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"])
```
|
Antony/mint_model | []
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}
} | 0 | 2022-12-29T13:21:07Z | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- ivensamdh/autotrain-data-age3
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 5.065800795931951
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2658279907
- CO2 Emissions (in grams): 5.0658
## Validation Metrics
- Loss: 0.895
- Accuracy: 0.770
- Macro F1: 0.768
- Micro F1: 0.770
- Weighted F1: 0.768
- Macro Precision: 0.773
- Micro Precision: 0.770
- Weighted Precision: 0.773
- Macro Recall: 0.770
- Micro Recall: 0.770
- Weighted Recall: 0.770 |
Anubhav23/indianlegal | []
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} | 0 | 2022-12-29T13:21:20Z | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- food
widget:
- text: a photo of jirostyle ramen noodles in the park
---
# DreamBooth model for the jirostyle concept trained by Prgckwb on the Prgckwb/jiro-style-ramen dataset.
This is a Stable Diffusion model fine-tuned on the jirostyle concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of jirostyle ramen noodles**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `ramen noodles` images for the food theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Prgckwb/JiroStyle-ramen')
prompt = "a photo of jirostyle ramen noodles"
image = pipeline(prompt).images[0]
```
## Generated Images
**a watercolor painting of jirostyle ramen noodles**
**a photo of a dog eating jirostyle ramen noodles in the park**
**a photo of jirostyle ramen noodles on the table**
|
Anupam/QuestionClassifier | []
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} | 0 | 2022-12-29T13:24:29Z | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Description
Anything Elynia Diffusion is a latent text-to-image diffusion model based on Anything 3.0 and then fine-tuned on the main character of 'Battle for Wesnoth' add-ons using Dreambooth. This model has been created to explore the possibilities and limitations of Dreambooth training and to study how it learns when low-resolution pixelart videogame sprites are added to the dataset in addition to realistic artwork.
To use this style in your generations, add `1girl, elynia` to the prompts.
Dreambooth hyperparameters
```sh
export MODEL_NAME="/home/{USER}/kml/models1/"
export INSTANCE_DIR="/home/{USER}/kml/datasets/objects/elyniaA"
export CLASS_DIR="/home/{USER}/kml/datasets/objects/elyniaX"
export OUTPUT_DIR="/home/{USER}/kml/models2/"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="1girl, elynia" \
--class_prompt="1girl, faerie girl, very long hair, pink hair, yellow eyes, detailed green dress, brown skirt, detached long sleeves, translucent green blue diamond-shaped butterfly wings" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800 \
--mixed_precision 'no' \
--train_text_encoder
```
Model Description
License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the model to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license https://huggingface.co/stabilityai/stable-diffusion-2
Downstream Uses
This model can be used for entertainment purposes and as a generative art assistant.
Acknowledgements
This project would not have been possible without the incredible work by the CompVis Researchers, Wesnoth devs, artists and user made content makers.
The dataset for training currently resides here https://drive.google.com/drive/folders/1-sa5eQ9ZgoW0hGu-jYhzIJs0qPpzeFLg?usp=share_link. |
Apisate/DialoGPT-small-jordan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 12 | null |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: it
datasets:
- lmqg/qg_itquad
pipeline_tag: text2text-generation
tags:
- answer extraction
widget:
- text: "<hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento."
example_title: "Answering Extraction Example 1"
- text: "<hl> Furono introdotti autocarri compatti, come la Toyota Hilux e il Datsun Truck, seguiti dal camion Mazda (venduto come il Ford Courier), e l' Isuzu costruito Chevrolet LUV. <hl> Mitsubishi rebranded il suo Forte come Dodge D-50 pochi anni dopo la crisi petrolifera. Mazda, Mitsubishi e Isuzu avevano partnership congiunte rispettivamente con Ford, Chrysler e GM. In seguito i produttori americani introdussero le loro sostituzioni nazionali (Ford Ranger, Dodge Dakota e la Chevrolet S10/GMC S-15), ponendo fine alla loro politica di importazione vincolata."
example_title: "Answering Extraction Example 2"
model-index:
- name: lmqg/mt5-small-itquad-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_itquad
type: default
args: default
metrics:
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 24.72
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 43.93
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 40.39
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 90.01
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 80.28
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 70.41
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 55.07
---
# Model Card of `lmqg/mt5-small-itquad-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for answer extraction on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
- **Language:** it
- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="it", model="lmqg/mt5-small-itquad-ae")
# model prediction
answers = model.generate_a("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-ae")
output = pipe("<hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.")
```
## Evaluation
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 55.07 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| AnswerF1Score | 70.41 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| BERTScore | 90.01 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_1 | 38.56 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_2 | 32.74 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_3 | 28.58 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| Bleu_4 | 24.72 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| METEOR | 40.39 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| MoverScore | 80.28 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
| ROUGE_L | 43.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_itquad
- dataset_name: default
- input_types: ['paragraph_sentence']
- output_types: ['answer']
- prefix_types: None
- model: google/mt5-small
- max_length: 512
- max_length_output: 32
- epoch: 17
- batch: 32
- lr: 0.0005
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-itquad-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
ArBert/roberta-base-finetuned-ner-gmm-twitter | []
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} | 0 | 2022-12-29T14:11:28Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: ru_propaganda_model_with_foreign_agent_mask
results: []
---
<!-- 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. -->
# ru_propaganda_model_with_foreign_agent_mask
This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0659
- Validation Loss: 0.3494
- Train Accuracy: 0.9
- Epoch: 4
## 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': 2e-05, 'decay_steps': 370, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6139 | 0.3611 | 0.86 | 0 |
| 0.2522 | 0.3060 | 0.86 | 1 |
| 0.1836 | 0.2757 | 0.92 | 2 |
| 0.1020 | 0.3094 | 0.92 | 3 |
| 0.0659 | 0.3494 | 0.9 | 4 |
### Framework versions
- Transformers 4.25.1
- TensorFlow 2.9.2
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ArBert/roberta-base-finetuned-ner-gmm | []
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} | 0 | 2022-12-29T14:13:01Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 15.03 +/- 87.72
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
To learn to code your own PPO agent and train it Unit 8 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': False
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': 'KeWangRL'
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.00035
'num_envs': 8
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.015
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': 0.015
'repo_id': 'kewangRL/LunarLander-v2'
'batch_size': 1024
'minibatch_size': 256}
```
|
ArBert/roberta-base-finetuned-ner-kmeans-twitter | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
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}
}
} | 10 | 2022-12-29T14:20:24Z | ---
license: openrail
library_name: diffusers
tags:
- TPU
- JAX
- Flax
- stable-diffusion
- text-to-image
- text-to-audio
language:
- en
--- |
ArBert/roberta-base-finetuned-ner | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
} | 3 | 2022-12-29T14:25:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model_mt5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-es
split: train
args: en-es
metrics:
- name: Bleu
type: bleu
value: 0.0004
---
<!-- 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. -->
# my_awesome_opus_books_model_mt5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: 0.0004
- Gen Len: 2.3199
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 0.0 | 1.0 | 4674 | nan | 0.0004 | 2.3199 |
| 0.0 | 2.0 | 9348 | nan | 0.0004 | 2.3199 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
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