modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 18:27:06
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
muhammadravi251001/fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-base-without-ITTL-without-freeze-LR-1e-05
|
muhammadravi251001
| 2023-03-13T13:55:12Z | 92 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-13T13:52:27Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-base-without-ITTL-without-freeze-LR-1e-05
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. -->
# fine-tuned-DatasetQAS-IDK-MRC-with-xlm-roberta-base-without-ITTL-without-freeze-LR-1e-05
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
Luisfrdz/a2c-AntBulletEnv-v0
|
Luisfrdz
| 2023-03-13T13:51:55Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T13:50:43Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 2487.65 +/- 37.31
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mrm8488/roberta-base-finetuned-OIG-mod-3
|
mrm8488
| 2023-03-13T13:47:04Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-13T10:25:37Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: roberta-base-finetuned-OIG-mod-3
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. -->
# roberta-base-finetuned-OIG-mod-3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3807
- F1: 0.8758
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.7135 | 0.36 | 5000 | 0.7030 | 0.6948 |
| 0.6783 | 0.71 | 10000 | 0.6782 | 0.7053 |
| 0.6073 | 1.07 | 15000 | 0.6356 | 0.7350 |
| 0.5827 | 1.42 | 20000 | 0.6089 | 0.7382 |
| 0.5701 | 1.78 | 25000 | 0.5778 | 0.7595 |
| 0.4856 | 2.13 | 30000 | 0.5742 | 0.7718 |
| 0.4651 | 2.49 | 35000 | 0.5368 | 0.7869 |
| 0.4634 | 2.84 | 40000 | 0.5049 | 0.8024 |
| 0.3913 | 3.2 | 45000 | 0.4973 | 0.8103 |
| 0.3877 | 3.55 | 50000 | 0.4655 | 0.8237 |
| 0.364 | 3.91 | 55000 | 0.4406 | 0.8411 |
| 0.3198 | 4.26 | 60000 | 0.4429 | 0.8456 |
| 0.3047 | 4.62 | 65000 | 0.4108 | 0.8595 |
| 0.2821 | 4.97 | 70000 | 0.3979 | 0.8653 |
| 0.2548 | 5.33 | 75000 | 0.3903 | 0.8713 |
| 0.2475 | 5.68 | 80000 | 0.3807 | 0.8758 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Inesence/donut-base-finetuned-Latvian-receipts
|
Inesence
| 2023-03-13T13:28:18Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-03-13T13:03:16Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-finetuned-Latvian-receipts
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. -->
# donut-base-finetuned-Latvian-receipts
This model is a fine-tuned version of [naver-clova-ix/donut-base-finetuned-cord-v2](https://huggingface.co/naver-clova-ix/donut-base-finetuned-cord-v2) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ELITE-library/ELITE
|
ELITE-library
| 2023-03-13T13:22:00Z | 0 | 5 | null |
[
"arxiv:2302.13848",
"region:us"
] | null | 2023-03-08T06:22:44Z |
# ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
<a href="https://arxiv.org/pdf/2302.13848.pdf"><img src="https://img.shields.io/badge/arXiv-2302.13848-b31b1b.svg" height=22.5></a>
<a href="https://huggingface.co/spaces/ELITE-library/ELITE"><img src="https://img.shields.io/static/v1?label=HuggingFace&message=gradio demo&color=darkgreen" height=22.5></a>
## Method Details
Given an image indicates the target concept (usually an object), we propose a learning-based encoder ELITE to encode the visual concept into the textual embeddings, which can be further flexibly composed into new scenes. It consists of two modules: (a) a global mapping network is first trained to encode a concept image into multiple textual word embeddings, where one primary word (w0) for well-editable concept and other auxiliary words (w1ยทยทยทN) to exclude irrelevant disturbances. (b) A local mapping network is further trained, which projects the foreground object into textual feature space to provide local details.
## Getting Started
### Environment Setup
```shell
git clone https://github.com/csyxwei/ELITE.git
cd ELITE
conda create -n elite python=3.9
conda activate elite
pip install -r requirements.txt
```
### Pretrained Models
We provide the pretrained checkpoints in [Google Drive](https://drive.google.com/drive/folders/1VkiVZzA_i9gbfuzvHaLH2VYh7kOTzE0x?usp=sharing). One can download them and save to the directory `checkpoints`.
### Setting up HuggingFace
Our code is built on the [diffusers](https://github.com/huggingface/diffusers/) version of Stable Diffusion, you need to accept the [model license](https://huggingface.co/CompVis/stable-diffusion-v1-4) before downloading or using the weights. In our experiments, we use model version v1-4.
You have to be a registered user in Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
Run the following command to authenticate your token
```shell
huggingface-cli login
```
If you have already cloned the repo, then you won't need to go through these steps.
### Customized Generation
We provide some testing images in [test_datasets](./test_datasets), which contains both images and object masks. For testing, you can run,
```
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR='./test_datasets/'
CUDA_VISIBLE_DEVICES=0 python inference_local.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--test_data_dir=$DATA_DIR \
--output_dir="./outputs/local_mapping" \
--suffix="object" \
--template="a photo of a S" \
--llambda="0.8" \
--global_mapper_path="./checkpoints/global_mapper.pt" \
--local_mapper_path="./checkpoints/local_mapper.pt"
```
or you can use the shell script,
```
bash inference_local.sh
```
If you want to test your customized dataset, you should align the image to ensure the object is at the center of image, and also provide the corresponding object mask. The object mask can be obtained by [image-matting-app](https://huggingface.co/spaces/SankarSrin/image-matting-app), or other image matting methods.
## Training
### Preparing Dataset
We use the **test** dataset of Open-Images V6 to train our ELITE. You can prepare the dataset as follows:
- Download Open-Images test dataset from [CVDF's site](https://github.com/cvdfoundation/open-images-dataset#download-images-with-bounding-boxes-annotations) and unzip it to the directory `datasets/Open_Images/images/test`.
- Download attribute names file `oidv6-attributes-description.csv` of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and save it to the directory `datasets/Open_Images/annotations/`.
- Download bbox annotations file `test-annotations-bbox.csv` of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and save it to the directory `datasets/Open_Images/annotations/`.
- Download segmentation annotations of Open-Images test dataset from [Open-Images official site](https://storage.googleapis.com/openimages/web/download_v7.html#download-manually) and unzip them to the directory `datasets/Open_Images/segs/test`. And put the `test-annotations-object-segmentation.csv` into `datasets/Open_Images/annotations/`.
- Obtain the mask bbox by running the following command:
```shell
python data_scripts/cal_bbox_by_seg.py
```
The final data structure is like this:
```
datasets
โโโ Open_Images
โ โโโ annotations
โ โ โโโ oidv6-class-descriptions.csv
โ โ โโโ test-annotations-object-segmentation.csv
โ โ โโโ test-annotations-bbox.csv
โ โโโ images
โ โ โโโ test
โ โ โ โโโ xxx.jpg
โ โ โ โโโ ...
โ โโโ segs
โ โ โโโ test
โ โ โ โโโ xxx.png
โ โ โ โโโ ...
โ โ โโโ test_bbox_dict.npy
```
### Training Global Mapping Network
To train the global mapping network, you can run,
```Shell
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR='./datasets/Open_Images/'
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --config_file 4_gpu.json --main_process_port 25656 train_global.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--placeholder_token="S" \
--resolution=512 \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--max_train_steps=200000 \
--learning_rate=1e-06 --scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="./elite_experiments/global_mapping" \
--save_steps 200
```
or you can use the shell script,
```shell
bash train_global.sh
```
### Training Local Mapping Network
After the global mapping network is trained, you can train the local mapping network by running,
```Shell
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR='/home/weiyuxiang/datasets/Open_Images/'
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch --config_file 4_gpu.json --main_process_port 25657 train_local.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--placeholder_token="S" \
--resolution=512 \
--train_batch_size=2 \
--gradient_accumulation_steps=4 \
--max_train_steps=200000 \
--learning_rate=1e-5 --scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--global_mapper_path "./elite_experiments/global_mapping/mapper_070000.pt" \
--output_dir="./elite_experiments/local_mapping" \
--save_steps 200
```
or you can use the shell script,
```shell
bash train_local.sh
```
## Citation
```
@article{wei2023elite,
title={ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation},
author={Wei, Yuxiang and Zhang, Yabo and Ji, Zhilong and Bai, Jinfeng and Zhang, Lei and Zuo, Wangmeng},
journal={arXiv preprint arXiv:2302.13848},
year={2023}
}
```
## Acknowledgements
This code is built on [diffusers](https://github.com/huggingface/diffusers/) version of [Stable Diffusion](https://github.com/CompVis/stable-diffusion). We thank the authors for sharing the codes.
|
MrDivakaruni/a2c-PandaReachDense-v2
|
MrDivakaruni
| 2023-03-13T13:17:10Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-05T08:42:30Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.49 +/- 0.53
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
birdy654/vincent-diffusion
|
birdy654
| 2023-03-13T13:16:04Z | 0 | 0 | null |
[
"text-to-image",
"license:cc-by-4.0",
"region:us"
] |
text-to-image
| 2023-03-13T11:46:41Z |
---
license: cc-by-4.0
tags:
- text-to-image
widget:
- text: "A beautiful sunset landscape, highly detailed painting in the style of JBVNCMOD"
---
|
priyankloco/swinv2-tiny-patch4-window8-256-finetuned_swinv2tiny-autotags-256
|
priyankloco
| 2023-03-13T12:52:46Z | 146 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-13T12:21:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finetuned_swinv2tiny-autotags-256
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: 0.965482233502538
---
<!-- 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. -->
# swinv2-tiny-patch4-window8-256-finetuned_swinv2tiny-autotags-256
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1115
- Accuracy: 0.9655
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6169 | 0.99 | 61 | 1.1018 | 0.6701 |
| 0.7747 | 1.99 | 122 | 0.4571 | 0.8670 |
| 0.6088 | 2.99 | 183 | 0.3002 | 0.9198 |
| 0.3908 | 3.99 | 244 | 0.2334 | 0.9299 |
| 0.399 | 4.99 | 305 | 0.2138 | 0.9320 |
| 0.2969 | 5.99 | 366 | 0.1650 | 0.9492 |
| 0.2743 | 6.99 | 427 | 0.1514 | 0.9533 |
| 0.2947 | 7.99 | 488 | 0.1428 | 0.9513 |
| 0.2304 | 8.99 | 549 | 0.1541 | 0.9523 |
| 0.1957 | 9.99 | 610 | 0.1256 | 0.9604 |
| 0.1645 | 10.99 | 671 | 0.1138 | 0.9645 |
| 0.2317 | 11.99 | 732 | 0.1140 | 0.9655 |
| 0.1001 | 12.99 | 793 | 0.1068 | 0.9706 |
| 0.1564 | 13.99 | 854 | 0.1119 | 0.9675 |
| 0.1386 | 14.99 | 915 | 0.1115 | 0.9655 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.10.2+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
GuiGel/beto-uncased-flert-finetune-meddocan
|
GuiGel
| 2023-03-13T12:34:06Z | 5 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"region:us"
] |
token-classification
| 2022-11-07T14:24:39Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("GuiGel/beto-finetune-meddocan")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
|
Kizi-Art/Cloudflared
|
Kizi-Art
| 2023-03-13T12:33:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-13T12:31:22Z |
# sd-webui-tunnels
Tunneling extension for [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
## Usage
### [cloudflared](https://try.cloudflare.com/)
add `--cloudflared` to commandline options.
### [localhost.run](https://localhost.run/)
add `--localhostrun` to commandline options.
### [remote.moe](https://github.com/fasmide/remotemoe)
add `--remotemoe` to commandline options.
The feature of `remote.moe` is that as long as the same ssh key is used, the same url is generated.
The ssh keys for `localhost.run` and `remote.moe` are created with the name `id_rsa` in the script's root folder. However, if there is a problem with the write permission, it is created in a temporary folder instead, so a different url is created each time.
|
dmargutierrez/distilbert-base-uncased-mapa-ner-coarse_grained-v2
|
dmargutierrez
| 2023-03-13T12:28:27Z | 123 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-13T10:43:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-mapa-ner-coarse_grained-v2
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-mapa-ner-coarse_grained-v2
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: 0.1627
- Precision: 0.7898
- Recall: 0.4843
- F1: 0.6004
- Accuracy: 0.9857
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0354 | 1.0 | 1739 | 0.0834 | 0.7023 | 0.4612 | 0.5568 | 0.9840 |
| 0.0255 | 2.0 | 3478 | 0.1034 | 0.8172 | 0.4355 | 0.5682 | 0.9852 |
| 0.0168 | 3.0 | 5217 | 0.0969 | 0.7714 | 0.4588 | 0.5754 | 0.9848 |
| 0.0132 | 4.0 | 6956 | 0.1042 | 0.7477 | 0.4838 | 0.5875 | 0.9852 |
| 0.0112 | 5.0 | 8695 | 0.1109 | 0.7421 | 0.4863 | 0.5876 | 0.9849 |
| 0.0085 | 6.0 | 10434 | 0.1076 | 0.7194 | 0.4951 | 0.5865 | 0.9850 |
| 0.0067 | 7.0 | 12173 | 0.1343 | 0.7828 | 0.4587 | 0.5784 | 0.9849 |
| 0.0047 | 8.0 | 13912 | 0.1252 | 0.7425 | 0.4840 | 0.5860 | 0.9853 |
| 0.0045 | 9.0 | 15651 | 0.1410 | 0.7943 | 0.4615 | 0.5838 | 0.9852 |
| 0.0035 | 10.0 | 17390 | 0.1311 | 0.7624 | 0.4929 | 0.5987 | 0.9857 |
| 0.003 | 11.0 | 19129 | 0.1494 | 0.8059 | 0.4691 | 0.5930 | 0.9855 |
| 0.0025 | 12.0 | 20868 | 0.1436 | 0.7674 | 0.4852 | 0.5945 | 0.9856 |
| 0.002 | 13.0 | 22607 | 0.1513 | 0.7778 | 0.4741 | 0.5891 | 0.9852 |
| 0.0014 | 14.0 | 24346 | 0.1577 | 0.7986 | 0.4726 | 0.5938 | 0.9855 |
| 0.0016 | 15.0 | 26085 | 0.1573 | 0.7802 | 0.4766 | 0.5917 | 0.9855 |
| 0.0011 | 16.0 | 27824 | 0.1599 | 0.7917 | 0.4723 | 0.5916 | 0.9856 |
| 0.0012 | 17.0 | 29563 | 0.1601 | 0.7848 | 0.4867 | 0.6008 | 0.9857 |
| 0.001 | 18.0 | 31302 | 0.1572 | 0.7614 | 0.4939 | 0.5991 | 0.9856 |
| 0.0011 | 19.0 | 33041 | 0.1602 | 0.7858 | 0.4870 | 0.6013 | 0.9857 |
| 0.0009 | 20.0 | 34780 | 0.1627 | 0.7898 | 0.4843 | 0.6004 | 0.9857 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
BlinkDL/rwkv-4-pile-3b
|
BlinkDL
| 2023-03-13T12:19:34Z | 0 | 42 | null |
[
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"dataset:the_pile",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2022-09-14T14:04:13Z |
---
language:
- en
tags:
- pytorch
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- the_pile
---
# RWKV-4 3B
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 3B is a L32-D2560 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
Use https://github.com/BlinkDL/ChatRWKV to run it.
RWKV-4-Pile-3B-20221110-ctx4096.pth (RECOMMENDED) : Fine-tuned to ctx_len 4096.
* LAMBADA ppl 5.25, acc 63.96%
* PIQA acc 74.16%
* SC2016 acc 70.71%
* Hellaswag acc_norm 59.89%
* ctx_len = 4096 n_layer = 32 n_embd = 2560
RWKV-4-Pile-3B-20221008-8023.pth : Trained on the Pile for 331B tokens.
* Pile loss 1.9469
* LAMBADA ppl 5.24, acc 63.94%
* PIQA acc 73.72%
* SC2016 acc 70.28%
* Hellaswag acc_norm 59.63%
* ctx_len = 1024 n_layer = 32 n_embd = 2560
### Instruct-test models: only useful if you construct your prompt following dataset templates
Note I am using "Q: instruct\n\nA: result" prompt for all instructs.
RWKV-4-Pile-3B-Instruct-test1
instruct-tuned on https://huggingface.co/datasets/bigscience/xP3all/viewer/en/train
RWKV-4-Pile-3B-Instruct-test2
instruct-tuned on https://huggingface.co/datasets/Muennighoff/flan & NIv2
### Chinese models
RWKV-4-Pile-3B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)
RWKV-4-Pile-3B-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.)
## Note: 4 / 4a / 4b models ARE NOT compatible. Use RWKV-4 unless you know what you are doing.
|
BlinkDL/rwkv-4-pile-169m
|
BlinkDL
| 2023-03-13T12:19:10Z | 0 | 10 | null |
[
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"dataset:the_pile",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2022-07-28T08:36:14Z |
---
language:
- en
tags:
- pytorch
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- the_pile
---
# RWKV-4 169M
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
## Model Description
RWKV-4 169M is a L12-D768 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.
Use https://github.com/BlinkDL/ChatRWKV to run it.
ctx_len = 1024
n_layer = 12
n_embd = 768
Final checkpoint:
RWKV-4-Pile-169M-20220807-8023.pth : Trained on the Pile for 332B tokens.
* Pile loss 2.5355
* LAMBADA ppl 29.33, acc 32.99%
* PIQA acc 65.07%
* SC2016 acc 58.79%
* Hellaswag acc_norm 32.26%
With tiny attention (--tiny_att_dim 256 --tiny_att_layer 9):
RWKV-4a-Pile-170M-20221209-7955.pth
* Pile loss 2.4702
* LAMBADA ppl 21.42, acc 38.23%
* PIQA acc 63.76%
* SC2016 acc 59.06%
* Hellaswag acc_norm 32.40%
RWKV-4b-Pile-171M-20230202-7922.pth (--my_testing 'a')
* Pile loss 2.4222
* LAMBADA ppl 22.02, acc 38.56%
* PIQA acc 64.04%
* SC2016 acc 59.91%
* Hellaswag acc_norm 33.33%
|
reyhanemyr/distilbert-base-cased-finetuned-paper
|
reyhanemyr
| 2023-03-13T12:18:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-13T12:12:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-finetuned-paper
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-cased-finetuned-paper
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1417
- Precision: 0.5690
- Recall: 0.6226
- F1: 0.5946
- Accuracy: 0.9790
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 62 | 0.1183 | 0.3810 | 0.3019 | 0.3368 | 0.9702 |
| No log | 2.0 | 124 | 0.0923 | 0.3986 | 0.5189 | 0.4508 | 0.9730 |
| No log | 3.0 | 186 | 0.0808 | 0.5 | 0.5943 | 0.5431 | 0.9765 |
| No log | 4.0 | 248 | 0.0951 | 0.6042 | 0.5472 | 0.5743 | 0.9790 |
| No log | 5.0 | 310 | 0.0939 | 0.5794 | 0.5849 | 0.5822 | 0.9795 |
| No log | 6.0 | 372 | 0.0949 | 0.5508 | 0.6132 | 0.5804 | 0.9792 |
| No log | 7.0 | 434 | 0.1004 | 0.5075 | 0.6415 | 0.5667 | 0.9782 |
| No log | 8.0 | 496 | 0.1110 | 0.5403 | 0.6321 | 0.5826 | 0.9784 |
| 0.0979 | 9.0 | 558 | 0.1139 | 0.5517 | 0.6038 | 0.5766 | 0.9789 |
| 0.0979 | 10.0 | 620 | 0.1153 | 0.5727 | 0.5943 | 0.5833 | 0.9795 |
| 0.0979 | 11.0 | 682 | 0.1238 | 0.5238 | 0.6226 | 0.5690 | 0.9776 |
| 0.0979 | 12.0 | 744 | 0.1249 | 0.5478 | 0.5943 | 0.5701 | 0.9786 |
| 0.0979 | 13.0 | 806 | 0.1263 | 0.5323 | 0.6226 | 0.5739 | 0.9786 |
| 0.0979 | 14.0 | 868 | 0.1303 | 0.5810 | 0.5755 | 0.5782 | 0.9792 |
| 0.0979 | 15.0 | 930 | 0.1358 | 0.4929 | 0.6509 | 0.5610 | 0.9773 |
| 0.0979 | 16.0 | 992 | 0.1305 | 0.5766 | 0.6038 | 0.5899 | 0.9793 |
| 0.0033 | 17.0 | 1054 | 0.1321 | 0.5323 | 0.6226 | 0.5739 | 0.9779 |
| 0.0033 | 18.0 | 1116 | 0.1353 | 0.5726 | 0.6321 | 0.6009 | 0.9789 |
| 0.0033 | 19.0 | 1178 | 0.1355 | 0.5462 | 0.6132 | 0.5778 | 0.9793 |
| 0.0033 | 20.0 | 1240 | 0.1346 | 0.5556 | 0.6132 | 0.5830 | 0.9796 |
| 0.0033 | 21.0 | 1302 | 0.1386 | 0.5403 | 0.6321 | 0.5826 | 0.9784 |
| 0.0033 | 22.0 | 1364 | 0.1389 | 0.5508 | 0.6132 | 0.5804 | 0.9790 |
| 0.0033 | 23.0 | 1426 | 0.1376 | 0.55 | 0.6226 | 0.5841 | 0.9790 |
| 0.0033 | 24.0 | 1488 | 0.1394 | 0.5641 | 0.6226 | 0.5919 | 0.9796 |
| 0.0012 | 25.0 | 1550 | 0.1408 | 0.55 | 0.6226 | 0.5841 | 0.9789 |
| 0.0012 | 26.0 | 1612 | 0.1413 | 0.5739 | 0.6226 | 0.5973 | 0.9792 |
| 0.0012 | 27.0 | 1674 | 0.1417 | 0.5455 | 0.6226 | 0.5815 | 0.9787 |
| 0.0012 | 28.0 | 1736 | 0.1424 | 0.5455 | 0.6226 | 0.5815 | 0.9787 |
| 0.0012 | 29.0 | 1798 | 0.1419 | 0.5546 | 0.6226 | 0.5867 | 0.9790 |
| 0.0012 | 30.0 | 1860 | 0.1417 | 0.5690 | 0.6226 | 0.5946 | 0.9790 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ljl2020/roberta-finetuned-subjqa-movies_2
|
ljl2020
| 2023-03-13T12:16:52Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-13T12:14:16Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-subjqa-movies_2
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. -->
# roberta-finetuned-subjqa-movies_2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Lajeremi/distilbert-base-uncased-finetuned-emotion
|
Lajeremi
| 2023-03-13T12:00:42Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-13T11:49:25Z |
---
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: 0.2205
- Accuracy: 0.9255
- F1: 0.9255
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8511 | 1.0 | 250 | 0.3244 | 0.9075 | 0.9040 |
| 0.2535 | 2.0 | 500 | 0.2205 | 0.9255 | 0.9255 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
GodfreyJ/play
|
GodfreyJ
| 2023-03-13T11:58:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-13T11:57:27Z |
A little blue boy standing on a red rock orbitting around the sun
|
MrDivakaruni/Reinforce-pixelcopter_policy
|
MrDivakaruni
| 2023-03-13T11:48:24Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-09T09:03:42Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter_policy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 26.60 +/- 21.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Luisfrdz/ppo-Pyramids1
|
Luisfrdz
| 2023-03-13T11:44:56Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-13T11:44:50Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: Luisfrdz/ppo-Pyramids1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
dmargutierrez/distilbert-base-uncased-TASTESet-ner
|
dmargutierrez
| 2023-03-13T11:44:25Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"named_entity_recognition",
"food_recipes_ner",
"dataset:dmargutierrez/TASTESet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-13T09:56:15Z |
---
license: apache-2.0
tags:
- named_entity_recognition
- food_recipes_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-TASTESet-ner
results: []
datasets:
- dmargutierrez/TASTESet
---
<!-- 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-TASTESet-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [TASTESet](https://huggingface.co/datasets/dmargutierrez/TASTESet) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3816
- Precision: 0.8929
- Recall: 0.9229
- F1: 0.9076
- Accuracy: 0.9130
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 31 | 1.0797 | 0.6027 | 0.6903 | 0.6435 | 0.7063 |
| No log | 2.0 | 62 | 0.6402 | 0.7681 | 0.8295 | 0.7976 | 0.8304 |
| No log | 3.0 | 93 | 0.4899 | 0.8379 | 0.8789 | 0.8579 | 0.8728 |
| No log | 4.0 | 124 | 0.4232 | 0.8716 | 0.8994 | 0.8853 | 0.8912 |
| No log | 5.0 | 155 | 0.3883 | 0.8798 | 0.9043 | 0.8919 | 0.8992 |
| No log | 6.0 | 186 | 0.3848 | 0.8769 | 0.9103 | 0.8933 | 0.9004 |
| No log | 7.0 | 217 | 0.3684 | 0.8864 | 0.9123 | 0.8991 | 0.9046 |
| No log | 8.0 | 248 | 0.3650 | 0.8930 | 0.9182 | 0.9054 | 0.9087 |
| No log | 9.0 | 279 | 0.3628 | 0.8908 | 0.9197 | 0.9050 | 0.9096 |
| No log | 10.0 | 310 | 0.3674 | 0.8933 | 0.9165 | 0.9047 | 0.9093 |
| No log | 11.0 | 341 | 0.3668 | 0.8958 | 0.9177 | 0.9066 | 0.9120 |
| No log | 12.0 | 372 | 0.3717 | 0.8904 | 0.9234 | 0.9066 | 0.9120 |
| No log | 13.0 | 403 | 0.3693 | 0.8940 | 0.9197 | 0.9067 | 0.9126 |
| No log | 14.0 | 434 | 0.3805 | 0.8913 | 0.9239 | 0.9073 | 0.9135 |
| No log | 15.0 | 465 | 0.3788 | 0.8954 | 0.9202 | 0.9076 | 0.9123 |
| No log | 16.0 | 496 | 0.3803 | 0.8935 | 0.9231 | 0.9081 | 0.9122 |
| 0.3275 | 17.0 | 527 | 0.3814 | 0.8918 | 0.9229 | 0.9071 | 0.9126 |
| 0.3275 | 18.0 | 558 | 0.3823 | 0.8921 | 0.9241 | 0.9079 | 0.9123 |
| 0.3275 | 19.0 | 589 | 0.3827 | 0.8928 | 0.9224 | 0.9074 | 0.9124 |
| 0.3275 | 20.0 | 620 | 0.3816 | 0.8929 | 0.9229 | 0.9076 | 0.9130 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
chanelcolgate/vit-base-patch16-224-finetuned-flower
|
chanelcolgate
| 2023-03-13T11:43:16Z | 222 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-13T11:29:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
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. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
ochapeau/q-Taxi-v3
|
ochapeau
| 2023-03-13T11:41:42Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T11:16:57Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ochapeau/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"])
```
|
droid22/a2c-AntBulletEnv-v0
|
droid22
| 2023-03-13T11:38:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T11:36:44Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1457.85 +/- 32.18
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
aienthused/dqn-SpaceInvadersNoFrameskip-v4
|
aienthused
| 2023-03-13T11:11:40Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T11:11:03Z |
---
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: 454.00 +/- 166.40
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aienthused -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aienthused -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga aienthused
```
## 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)])
```
|
ehsanshfgh/persian
|
ehsanshfgh
| 2023-03-13T11:01:12Z | 0 | 0 |
asteroid
|
[
"asteroid",
"fa",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:gsdf/EasyNegative",
"dataset:nyanko7/LLaMA-65B",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-13T11:00:14Z |
---
license: creativeml-openrail-m
datasets:
- fka/awesome-chatgpt-prompts
- gsdf/EasyNegative
- nyanko7/LLaMA-65B
language:
- fa
metrics:
- bleurt
- accuracy
library_name: asteroid
---
|
reachrkr/ppo-LunarLander-v2
|
reachrkr
| 2023-03-13T10:54:43Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2022-08-19T08:12:15Z |
---
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: -139.57 +/- 78.56
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': 50000
'learning_rate': 0.00025
'num_envs': 4
'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': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'reachrkr/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
trpakov/vit-pneumonia
|
trpakov
| 2023-03-13T10:48:50Z | 239 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:chest-xray-classification",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-13T08:32:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- chest-xray-classification
metrics:
- accuracy
model-index:
- name: vit-pneumonia
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: chest-xray-classification
type: chest-xray-classification
config: full
split: validation
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.976824034334764
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-pneumonia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the chest-xray-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1086
- Accuracy: 0.9768
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0357 | 1.0 | 192 | 0.0955 | 0.9691 |
| 0.0404 | 2.0 | 384 | 0.0720 | 0.9751 |
| 0.0546 | 3.0 | 576 | 0.2275 | 0.9468 |
| 0.0113 | 4.0 | 768 | 0.1386 | 0.9648 |
| 0.0101 | 5.0 | 960 | 0.1212 | 0.9708 |
| 0.0003 | 6.0 | 1152 | 0.0929 | 0.9777 |
| 0.0002 | 7.0 | 1344 | 0.1051 | 0.9777 |
| 0.0002 | 8.0 | 1536 | 0.1075 | 0.9777 |
| 0.0002 | 9.0 | 1728 | 0.1084 | 0.9768 |
| 0.0002 | 10.0 | 1920 | 0.1086 | 0.9768 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
avoroshilov/a2c-PandaReachDense-v2
|
avoroshilov
| 2023-03-13T10:46:10Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T10:45:10Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.43 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
abbiekeats/q-FrozenLake-v1-4x4-noSlippery
|
abbiekeats
| 2023-03-13T10:26:33Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T10:26:31Z |
---
tags:
- FrozenLake-v1-4x4
- 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
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.59 +/- 0.49
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="abbiekeats/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"])
```
|
kunC/q-FrozenLake-v1-4x4-noSlippery
|
kunC
| 2023-03-13T10:24:34Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T10:21:10Z |
---
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="kunC/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"])
```
|
MikolajDeja/facebook-nllb-200-distilled-600M-pl-en-opus100-finetune
|
MikolajDeja
| 2023-03-13T10:22:29Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"dataset:opus100",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-04T10:10:22Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
datasets:
- opus100
model-index:
- name: facebook-nllb-200-distilled-600M-pl-en-opus100-finetune
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. -->
# facebook-nllb-200-distilled-600M-pl-en-opus100-finetune
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the opus100 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
hasarinduperera/ppo-LunarLander-v2-PyTorch
|
hasarinduperera
| 2023-03-13T10:22:12Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T10:00:18Z |
---
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: 105.02 +/- 94.28
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': 500000
'learning_rate': 0.00025
'num_envs': 4
'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': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'hasarinduperera/ppo-LunarLander-v2-PyTorch'
'batch_size': 512
'minibatch_size': 128}
```
|
cthiriet/a2c-PandaReachDense-v2
|
cthiriet
| 2023-03-13T10:16:31Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T10:13:48Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.15 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Endika99/NLP-TokenClass-NER
|
Endika99
| 2023-03-13T10:16:24Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-12T20:18:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-TokenClass-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: en
split: validation
args: en
metrics:
- name: Precision
type: precision
value: 0.8119634827633192
- name: Recall
type: recall
value: 0.8424996465431924
- name: F1
type: f1
value: 0.8269497640854844
- name: Accuracy
type: accuracy
value: 0.92547623848305
---
<!-- 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. -->
# NLP-TokenClass-NER
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3741
- Precision: 0.8120
- Recall: 0.8425
- F1: 0.8269
- Accuracy: 0.9255
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0902 | 1.0 | 2500 | 0.3741 | 0.8120 | 0.8425 | 0.8269 | 0.9255 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
markjoe7447/markjoeBlogg
|
markjoe7447
| 2023-03-13T10:07:29Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-13T10:07:29Z |
---
license: creativeml-openrail-m
---
|
huam/Reinforce-cartpole
|
huam
| 2023-03-13T09:56:48Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T09:56:35Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
cthiriet/a2c-AntBulletEnv-v0
|
cthiriet
| 2023-03-13T09:54:59Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T09:53:42Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 826.68 +/- 73.57
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
xiaozeng/lora_moebius
|
xiaozeng
| 2023-03-13T09:54:56Z | 0 | 0 | null |
[
"paddlepaddle",
"stable-diffusion",
"stable-diffusion-ppdiffusers",
"text-to-image",
"ppdiffusers",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-13T06:07:02Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of a necklace in the shape of Mobius belt
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - xiaozeng/lora_moebius
ๆฌไปๅบ็ LoRA ๆ้ๆฏๅบไบ runwayml/stable-diffusion-v1-5 ่ฎญ็ป่ๆฅ็๏ผๆไปฌ้็จ[DreamBooth](https://dreambooth.github.io/)็ๆๆฏๅนถไฝฟ็จ a photo of a necklace in the shape of Mobius belt ๆๆฌ่ฟ่กไบ่ฎญ็ปใ
|
ArthurZ/nllb-moe-128
|
ArthurZ
| 2023-03-13T09:53:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"nllb_moe",
"feature-extraction",
"nllb",
"nllb-moe",
"translation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",
"crh",
"cy",
"da",
"de",
"dik",
"dyu",
"dz",
"el",
"en",
"eo",
"et",
"eu",
"ee",
"fo",
"fj",
"fi",
"fon",
"fr",
"fur",
"fuv",
"gaz",
"gd",
"ga",
"gl",
"gn",
"gu",
"ht",
"ha",
"he",
"hi",
"hne",
"hr",
"hu",
"hy",
"ig",
"ilo",
"id",
"is",
"it",
"jv",
"ja",
"kab",
"kac",
"kam",
"kn",
"ks",
"ka",
"kk",
"kbp",
"kea",
"khk",
"km",
"ki",
"rw",
"ky",
"kmb",
"kmr",
"knc",
"kg",
"ko",
"lo",
"lij",
"li",
"ln",
"lt",
"lmo",
"ltg",
"lb",
"lua",
"lg",
"luo",
"lus",
"lvs",
"mag",
"mai",
"ml",
"mar",
"min",
"mk",
"mt",
"mni",
"mos",
"mi",
"my",
"nl",
"nn",
"nb",
"npi",
"nso",
"nus",
"ny",
"oc",
"ory",
"pag",
"pa",
"pap",
"pbt",
"pes",
"plt",
"pl",
"pt",
"prs",
"quy",
"ro",
"rn",
"ru",
"sg",
"sa",
"sat",
"scn",
"shn",
"si",
"sk",
"sl",
"sm",
"sn",
"sd",
"so",
"st",
"es",
"sc",
"sr",
"ss",
"su",
"sv",
"swh",
"szl",
"ta",
"taq",
"tt",
"te",
"tg",
"tl",
"th",
"ti",
"tpi",
"tn",
"ts",
"tk",
"tum",
"tr",
"tw",
"tzm",
"ug",
"uk",
"umb",
"ur",
"uzn",
"vec",
"vi",
"war",
"wo",
"xh",
"ydd",
"yo",
"yue",
"zh",
"zsm",
"zu",
"dataset:flores-200",
"arxiv:2207.04672",
"license:cc-by-nc-4.0",
"region:us"
] |
translation
| 2023-03-13T09:02:34Z |
---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fur
- fuv
- gaz
- gd
- ga
- gl
- gn
- gu
- ht
- ha
- he
- hi
- hne
- hr
- hu
- hy
- ig
- ilo
- id
- is
- it
- jv
- ja
- kab
- kac
- kam
- kn
- ks
- ka
- kk
- kbp
- kea
- khk
- km
- ki
- rw
- ky
- kmb
- kmr
- knc
- kg
- ko
- lo
- lij
- li
- ln
- lt
- lmo
- ltg
- lb
- lua
- lg
- luo
- lus
- lvs
- mag
- mai
- ml
- mar
- min
- mk
- mt
- mni
- mos
- mi
- my
- nl
- nn
- nb
- npi
- nso
- nus
- ny
- oc
- ory
- pag
- pa
- pap
- pbt
- pes
- plt
- pl
- pt
- prs
- quy
- ro
- rn
- ru
- sg
- sa
- sat
- scn
- shn
- si
- sk
- sl
- sm
- sn
- sd
- so
- st
- es
- sc
- sr
- ss
- su
- sv
- swh
- szl
- ta
- taq
- tt
- te
- tg
- tl
- th
- ti
- tpi
- tn
- ts
- tk
- tum
- tr
- tw
- tzm
- ug
- uk
- umb
- ur
- uzn
- vec
- vi
- war
- wo
- xh
- ydd
- yo
- yue
- zh
- zsm
- zu
language_details: "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn"
tags:
- nllb
- nllb-moe
- translation
license: "cc-by-nc-4.0"
datasets:
- flores-200
metrics:
- bleu
- spbleu
- chrf++
inference: false
---
# NLLB-MoE
This is the model card of NLLB-MoE variant.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues
The NLLB model was presented in [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by Marta R. Costa-jussร , James Cross, Onur รelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula,
Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews,
Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmรกn, Philipp Koehn, Alexandre Mourachko, Christophe Ropers,
Safiyyah Saleem, Holger Schwenk, and Jeff Wang.
## Generating with NLLB-MoE
The avalable checkpoints requires around 350GB of storage. Make sure to use `accelerate` if you do not have enough RAM on your machine.
While generating the target text set the `forced_bos_token_id` to the target language id. The following
example shows how to translate English to French using the *facebook/nllb-200-distilled-600M* model.
Note that we're using the BCP-47 code for French `fra_Latn`. See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
for the list of all BCP-47 in the Flores 200 dataset.
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-moe-54b")
>>> article = "UN Chief says there is no military solution in Syria"
>>> inputs = tokenizer(article, return_tensors="pt")
>>> translated_tokens = model.generate(
... **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["fra_Latn"], max_length=30
... )
>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
Le chef de l'ONU dit qu'il n'y a pas de solution militaire en Syrie
```
|
LinaTarasenko99/ADHD_Test_qa_model
|
LinaTarasenko99
| 2023-03-13T09:37:42Z | 130 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-13T09:36:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ADHD_Test_qa_model
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. -->
# ADHD_Test_qa_model
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: 1.0061
## 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 5.7461 |
| No log | 2.0 | 4 | 5.5441 |
| No log | 3.0 | 6 | 5.3226 |
| No log | 4.0 | 8 | 5.0725 |
| No log | 5.0 | 10 | 4.8020 |
| No log | 6.0 | 12 | 4.5135 |
| No log | 7.0 | 14 | 4.2225 |
| No log | 8.0 | 16 | 3.9429 |
| No log | 9.0 | 18 | 3.6847 |
| No log | 10.0 | 20 | 3.4510 |
| No log | 11.0 | 22 | 3.2467 |
| No log | 12.0 | 24 | 3.0685 |
| No log | 13.0 | 26 | 2.9113 |
| No log | 14.0 | 28 | 2.7682 |
| No log | 15.0 | 30 | 2.6341 |
| No log | 16.0 | 32 | 2.4968 |
| No log | 17.0 | 34 | 2.3575 |
| No log | 18.0 | 36 | 2.2179 |
| No log | 19.0 | 38 | 2.0802 |
| No log | 20.0 | 40 | 1.9476 |
| No log | 21.0 | 42 | 1.8254 |
| No log | 22.0 | 44 | 1.6981 |
| No log | 23.0 | 46 | 1.5769 |
| No log | 24.0 | 48 | 1.4611 |
| No log | 25.0 | 50 | 1.3675 |
| No log | 26.0 | 52 | 1.2925 |
| No log | 27.0 | 54 | 1.2285 |
| No log | 28.0 | 56 | 1.1718 |
| No log | 29.0 | 58 | 1.1221 |
| No log | 30.0 | 60 | 1.0865 |
| No log | 31.0 | 62 | 1.0644 |
| No log | 32.0 | 64 | 1.0428 |
| No log | 33.0 | 66 | 1.0304 |
| No log | 34.0 | 68 | 1.0209 |
| No log | 35.0 | 70 | 1.0109 |
| No log | 36.0 | 72 | 1.0079 |
| No log | 37.0 | 74 | 1.0096 |
| No log | 38.0 | 76 | 1.0071 |
| No log | 39.0 | 78 | 1.0064 |
| No log | 40.0 | 80 | 1.0061 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Surteng/rlstc_vsn
|
Surteng
| 2023-03-13T09:29:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-13T09:25:13Z |
---
license: creativeml-openrail-m
---
|
D0k-tor/ppo-Pyramids-Training
|
D0k-tor
| 2023-03-13T09:17:25Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-13T09:17:20Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: D0k-tor/ppo-Pyramids-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
AliChazz/GPT2_Fine_Tune_Requirement_Produce
|
AliChazz
| 2023-03-13T09:14:12Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-13T08:29:54Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: GPT2_Fine_Tune_Requirement_Produce
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. -->
# GPT2_Fine_Tune_Requirement_Produce
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 12
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ArthurZ/fairseq-nllb-moe
|
ArthurZ
| 2023-03-13T09:06:30Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-03-11T10:09:09Z |
# This repo shows how to convert a fairseq NLLB-MoE model to transformers and run a forward pass
As the `fairseq` repository is not really optimised to run inference out-of-the-box, make sure you have a very very big CPU/GPU RAM.
Around 600 GB are required to run an inference with the `fairseq` model, as you need to load the checkpoints (\~300GB) then build the model (\~300GB again), then finally you can load the checkpoints in the model.
## 0. Download the original checkpoints:
The checkpoints in this repository were obtained using the following command (ased on the instructions given on the fairseq repository):
```bash
wget --trust-remote-name path_to_nllb
tar -cf model.tar.zf
```
The NLLB checkpoints should noz
## 1. Install PyTorch
Use the following command:
```bash
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
```
## 2. Install fairseq
```bash
git clone https://github.com/facebookresearch/fairseq.git
cd fairscale
git checkout prefetch_fsdp_params_simple
pip3 install -e .
```
## 3. Clone this repo (click top right on "How to clone")
## 4. Run the inference script:
Convert the checkpoints on the fly using the conversion script. `transformers` is required to do this:
```bash
cd <path/to/cloned/repo>
python3 /home/arthur_huggingface_co/fairseq/weights/checkpoints/convert_nllb_moe_sharded_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path <dump_folder> --nllb_moe_checkpoint_path <nllb_checkpoint_path>
```
## 4. Run the inference script:
```bash
cd <path/to/cloned/repo>
bash run.sh
```
|
ammr/a2c-PandaReachDense-v2
|
ammr
| 2023-03-13T09:05:40Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T09:03:02Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.09 +/- 0.28
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Surteng/AOMx
|
Surteng
| 2023-03-13T09:05:18Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-01T02:21:09Z |
---
license: creativeml-openrail-m
---
|
alexbalandi/ppo-LunarLander-v2-4milsteps-200-envs
|
alexbalandi
| 2023-03-13T08:50:05Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-12T13:05:38Z |
---
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: 286.02 +/- 16.23
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
...
```
|
ochapeau/ppo-LunarLander-v2
|
ochapeau
| 2023-03-13T08:48:38Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T08:24:19Z |
---
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: 253.80 +/- 25.59
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
...
```
|
qiaoqian/my_awesome_model
|
qiaoqian
| 2023-03-13T08:41:15Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-13T08:18:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93084
---
<!-- 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_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2367
- Accuracy: 0.9308
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2302 | 1.0 | 1563 | 0.1915 | 0.9289 |
| 0.1474 | 2.0 | 3126 | 0.2367 | 0.9308 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
reachrkr/ppo-Pyramids
|
reachrkr
| 2023-03-13T08:30:28Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-03-13T08:30:22Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: reachrkr/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
utyug1/poca-SoccerTwos
|
utyug1
| 2023-03-13T08:24:14Z | 39 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-13T08:24:06Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: utyug1/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
ChaiML/gpt2_base_retry_and_continue_12m_reward_model
|
ChaiML
| 2023-03-13T08:22:54Z | 104 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-classification",
"reward_model",
"RLHF",
"en",
"arxiv:2303.06135",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-03T17:37:48Z |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-classification
tags:
- pytorch
- reward_model
- transformers
- RLHF
library_name: transformers
---
This is part of the Chai reward-model series, using the GPT2 architecture with a classification head, optimising for a user accepting the completion generated by the base model.
Its training dataset consists of purely user-generated content [retry_and_continue_50m_reward_model](https://huggingface.co/datasets/ChaiML/retry_and_continue_50m_reward_model), where a user has the option to decline the generated response via the retry button or end the conversation.
## Model details
- Developed by [Chai Research](https://www.chai-research.com/)
- Model type: Transformer-based Classification Model
- Language: English
- License: cc-by-nc-4.0
- Contact: for general correspondence, please email [hello@chai-research.com](mailto:hello@chai-research.com?subject=Huggingface%20Model%20Inquiry)
## Uses and limitations
### Intended use
This reward model was developed primarily for commercial purposes. It learns an inner representation of response quality rated by humans that can be used to conduct best-of-N sampling and Reinforcement Leanring with the PPO framework.
In addition to scientific uses, you may also further fine-tune and adapt this reward model for deployment, as long as your use is in accordance with the Creative Commons Attribution Non Commercial 4.0 (cc-by-nc-4.0) license, i.e. non-commercial use. This model works with the Transformers Library. If you decide to this pre-trained reward model as a basis for your fine-tuned model, please note that you need to conduct your own risk and bias assessment.
### Out-of-scope use
This reward model is **not** intended for deployment as-is. It is not a product and cannot be used for human-facing interactions without supervision.
This model **has not** been optimised for common reward-model objectives such as harmfulness, truthfulness and helpfulness, it is only trained based on user actions present on the Chai mobile app platform. Therefore, this model will **not** rank responses appropriately when evaluating on common open-sourced datasets. All base model responses within the training data were generated using an in-house variant of [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), therefore the model performance may degrade when the input is generated using other language models.
### How to use
This reward model can be loaded using the `AutoModelForSequenceClassification` functionality, with a GPT2 tokenizer where the `pad_token_id` is set to the EOS token id, padding sides need to be set according to the configurations used during model training.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForSequenceClassification.from_pretrained("ChaiML/gpt2_base_retry_and_continue_12m_reward_model")
tokenizer.pad_token_id = 50256
tokenizer.truncation_side = โleftโ
tokenizer.padding_side = โrightโ
tokens = self.eval_tokenizer(candidates, return_tensors='pt', return_attention_mask=True, padding='longest', truncation=True, max_length=256)
reward = model(**tokens).logits
```
## Model training
### Training dataset
This model was trained by randomly sampling 12 million rows out of the [ChaiML/retry_and_continue_50m_reward_model](https://huggingface.co/datasets/ChaiML/retry_and_continue_50m_reward_model) dataset.
The original dataset contains over 50 million rows of completions (chatbot responses), along with number of remaining user messages within their corresponding conversations and whether the user pressed the "retry" button (where the completion is rejected and resampled). The model which was used to generate these completions is a in-house variant of [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), with the following sampling parameters:
<figure style="width:30em">
| Parameters | Value |
| ---------------------- | ----------- |
| temperature | 0.72 |
| repetition_penalty | 1.13125 |
| max_new_tokens | 64 |
| top_p | 0.725 |
| top_k | 0 |
| eos_token_id | 198 |
| do_sample | True |
</figure>
### Training procedure
The `gpt2_base_retry_and_continue_12m_reward_model` was trained using a [gpt2](https://huggingface.co/gpt2) base model and a classification head with single output. Binary Cross Entropy loss was used. The model was trained on 4xA40 GPUs, 16 per device batch size and gradient accumulation of 1 (therefore the effective batch size is 64), with 1e-5 learning rate for 2 epochs for a total of 375,000 steps. Tensor parallelism and pipeline parallelism were used to distribute the model across GPUs. For evaluation metrics used during training, please see our [Weights and Biases Log](https://wandb.ai/jellywibble/reward).
### BibTeX entry
To cite this model:
```bibtex
@misc{
author = {Chai Research, Irvine, Boubert, Raina, Liusie, Mudupalli, Korshuk, Liu, Cremer, Assassi, C. Beauchamp, Lu, Rialan, W. Beauchamp},
title = {{Rewarding chatbots for real-world engagement with millions of users}},
howpublished = {\url{https://arxiv.org/abs/2303.06135}},
year = 2023,
month = Mar
}
```
If you use this model, we would love to hear about it! Reach out on [correspondence email](mailto:thomas@chai-research.com?subject=Chai%20Research%20Paper%20Enquiry) or Discord.
### Acknowledgements
This project would not have been possible without the support from members of [Seamless Capital](https://www.seamless-capital.com/)
We thank the following authors from the [Machine Intelligence Laboratory](https://mi.eng.cam.ac.uk/) for their collaboration:
- [Vysas Raina](https://www.linkedin.com/in/vyas-raina-71b226152/)
- [Adian Liusie](https://www.linkedin.com/in/adian-liusie-00b60511a/)
|
ChaiML/gpt2_large_retry_and_continue_12m_reward_model
|
ChaiML
| 2023-03-13T08:22:13Z | 176 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"reward_model",
"RLHF",
"text-classification",
"en",
"arxiv:2303.06135",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-06T01:14:30Z |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-classification
tags:
- pytorch
- reward_model
- transformers
- RLHF
library_name: transformers
---
This is part of the Chai reward-model series, using the GPT2 architecture with a classification head, optimising for a user accepting the completion generated by the base model.
Its training dataset consists of purely user-generated content [retry_and_continue_50m_reward_model](https://huggingface.co/datasets/ChaiML/retry_and_continue_50m_reward_model), where a user has the option to decline the generated response via the retry button or end the conversation.
## Model details
- Developed by [Chai Research](https://www.chai-research.com/)
- Model type: Transformer-based Classification Model
- Language: English
- License: cc-by-nc-4.0
- Contact: for general correspondence, please email [hello@chai-research.com](mailto:hello@chai-research.com?subject=Huggingface%20Model%20Inquiry)
## Uses and limitations
### Intended use
This reward model was developed primarily for commercial purposes. It learns an inner representation of response quality rated by humans that can be used to conduct best-of-N sampling and Reinforcement Leanring with the PPO framework.
In addition to scientific uses, you may also further fine-tune and adapt this reward model for deployment, as long as your use is in accordance with the Creative Commons Attribution Non Commercial 4.0 (cc-by-nc-4.0) license, i.e. non-commercial use. This model works with the Transformers Library. If you decide to this pre-trained reward model as a basis for your fine-tuned model, please note that you need to conduct your own risk and bias assessment.
### Out-of-scope use
This reward model is **not** intended for deployment as-is. It is not a product and cannot be used for human-facing interactions without supervision.
This model **has not** been optimised for common reward-model objectives such as harmfulness, truthfulness and helpfulness, it is only trained based on user actions present on the Chai mobile app platform. Therefore, this model will **not** rank responses appropriately when evaluating on common open-sourced datasets. All base model responses within the training data were generated using an in-house variant of [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), therefore the model performance may degrade when the input is generated using other language models.
### How to use
This reward model can be loaded using the `AutoModelForSequenceClassification` functionality, with a GPT2 tokenizer where the `pad_token_id` is set to the EOS token id, padding sides need to be set according to the configurations used during model training.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForSequenceClassification.from_pretrained("ChaiML/gpt2_large_retry_and_continue_12m_reward_model")
tokenizer.pad_token_id = 50256
tokenizer.truncation_side = โleftโ
tokenizer.padding_side = โrightโ
tokens = self.eval_tokenizer(candidates, return_tensors='pt', return_attention_mask=True, padding='longest', truncation=True, max_length=256)
reward = model(**tokens).logits
```
## Model training
### Training dataset
This model was trained by randomly sampling 12 million rows out of the [ChaiML/retry_and_continue_50m_reward_model](https://huggingface.co/datasets/ChaiML/retry_and_continue_50m_reward_model) dataset.
The original dataset contains over 50 million rows of completions (chatbot responses), along with number of remaining user messages within their corresponding conversations and whether the user pressed the "retry" button (where the completion is rejected and resampled). The model which was used to generate these completions is a in-house variant of [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), with the following sampling parameters:
<figure style="width:30em">
| Parameters | Value |
| ---------------------- | ----------- |
| temperature | 0.72 |
| repetition_penalty | 1.13125 |
| max_new_tokens | 64 |
| top_p | 0.725 |
| top_k | 0 |
| eos_token_id | 198 |
| do_sample | True |
</figure>
### Training procedure
The `gpt2_large_retry_and_continue_12m_reward_model` was trained using a [gpt2-large](https://huggingface.co/gpt2-large) base model and a classification head with single output. Binary Cross Entropy loss was used. The model was trained on 4xA40 GPUs, 16 per device batch size and gradient accumulation of 1 (therefore the effective batch size is 64), with 1e-5 learning rate for 2 epochs for a total of 375,000 steps. Tensor parallelism and pipeline parallelism were used to distribute the model across GPUs.
### BibTeX entry
To cite this model:
```bibtex
@misc{
author = {Chai Research, Irvine, Boubert, Raina, Liusie, Mudupalli, Korshuk, Liu, Cremer, Assassi, C. Beauchamp, Lu, Rialan, W. Beauchamp},
title = {{Rewarding chatbots for real-world engagement with millions of users}},
howpublished = {\url{https://arxiv.org/abs/2303.06135}},
year = 2023,
month = Mar
}
```
If you use this model, we would love to hear about it! Reach out on [correspondence email](mailto:thomas@chai-research.com?subject=Chai%20Research%20Paper%20Enquiry) or Discord.
### Acknowledgements
This project would not have been possible without the support from members of [Seamless Capital](https://www.seamless-capital.com/)
We thank the following authors from the [Machine Intelligence Laboratory](https://mi.eng.cam.ac.uk/) for their collaboration:
- [Vysas Raina](https://www.linkedin.com/in/vyas-raina-71b226152/)
- [Adian Liusie](https://www.linkedin.com/in/adian-liusie-00b60511a/)
|
ChaiML/gpt2_base_retry_and_continue_5m_reward_model
|
ChaiML
| 2023-03-13T08:21:07Z | 160 | 4 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-classification",
"reward_model",
"RLHF",
"en",
"arxiv:2303.06135",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-23T15:39:28Z |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-classification
tags:
- pytorch
- reward_model
- transformers
- RLHF
library_name: transformers
---
This is part of the Chai reward-model series, using the GPT2 architecture with a classification head, optimising for a user accepting the completion generated by the base model.
Its training dataset consists of purely user-generated content [retry_and_continue_50m_reward_model](https://huggingface.co/datasets/ChaiML/retry_and_continue_50m_reward_model), where a user has the option to decline the generated response via the retry button or end the conversation.
## Model details
- Developed by [Chai Research](https://www.chai-research.com/)
- Model type: Transformer-based Classification Model
- Language: English
- License: cc-by-nc-4.0
- Contact: for general correspondence, please email [hello@chai-research.com](mailto:hello@chai-research.com?subject=Huggingface%20Model%20Inquiry)
## Uses and limitations
### Intended use
This reward model was developed primarily for commercial purposes. It learns an inner representation of response quality rated by humans that can be used to conduct best-of-N sampling and Reinforcement Leanring with the PPO framework.
In addition to scientific uses, you may also further fine-tune and adapt this reward model for deployment, as long as your use is in accordance with the Creative Commons Attribution Non Commercial 4.0 (cc-by-nc-4.0) license, i.e. non-commercial use. This model works with the Transformers Library. If you decide to this pre-trained reward model as a basis for your fine-tuned model, please note that you need to conduct your own risk and bias assessment.
### Out-of-scope use
This reward model is **not** intended for deployment as-is. It is not a product and cannot be used for human-facing interactions without supervision.
This model **has not** been optimised for common reward-model objectives such as harmfulness, truthfulness and helpfulness, it is only trained based on user actions present on the Chai mobile app platform. Therefore, this model will **not** rank responses appropriately when evaluating on common open-sourced datasets. All base model responses within the training data were generated using an in-house variant of [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), therefore the model performance may degrade when the input is generated using other language models.
### How to use
This reward model can be loaded using the `AutoModelForSequenceClassification` functionality, with a GPT2 tokenizer where the `pad_token_id` is set to the EOS token id, padding sides need to be set according to the configurations used during model training.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForSequenceClassification.from_pretrained("ChaiML/gpt2_base_retry_and_continue_5m_reward_model")
tokenizer.pad_token_id = 50256
tokenizer.truncation_side = โleftโ
tokenizer.padding_side = โrightโ
tokens = self.eval_tokenizer(candidates, return_tensors='pt', return_attention_mask=True, padding='longest', truncation=True, max_length=256)
reward = model(**tokens).logits
```
## Model training
### Training dataset
This model was trained by randomly sampling 5 million rows out of the [ChaiML/retry_and_continue_50m_reward_model](https://huggingface.co/datasets/ChaiML/retry_and_continue_50m_reward_model) dataset.
The original dataset contains over 50 million rows of completions (chatbot responses), along with number of remaining user messages within their corresponding conversations and whether the user pressed the "retry" button (where the completion is rejected and resampled). The model which was used to generate these completions is a in-house variant of [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), with the following sampling parameters:
<figure style="width:30em">
| Parameters | Value |
| ---------------------- | ----------- |
| temperature | 0.72 |
| repetition_penalty | 1.13125 |
| max_new_tokens | 64 |
| top_p | 0.725 |
| top_k | 0 |
| eos_token_id | 198 |
| do_sample | True |
</figure>
### Training procedure
The `gpt2_base_retry_and_continue_5m_reward_model` was trained using a [gpt2](https://huggingface.co/gpt2) base model and a classification head with single output. Binary Cross Entropy loss was used. The model was trained on 4xA40 GPUs, 16 per device batch size and gradient accumulation of 1 (therefore the effective batch size is 64), with 1e-5 learning rate for 2 epochs for a total of 156,240 steps. Tensor parallelism and pipeline parallelism were used to distribute the model across GPUs. For evaluation metrics used during training, please see our [Weights and Biases Log](https://wandb.ai/jellywibble/reward).
### BibTeX entry
To cite this model:
```bibtex
@misc{
author = {Chai Research, Irvine, Boubert, Raina, Liusie, Mudupalli, Korshuk, Liu, Cremer, Assassi, C. Beauchamp, Lu, Rialan, W. Beauchamp},
title = {{Rewarding chatbots for real-world engagement with millions of users}},
howpublished = {\url{https://arxiv.org/abs/2303.06135}},
year = 2023,
month = Mar
}
```
If you use this model, we would love to hear about it! Reach out on [correspondence email](mailto:thomas@chai-research.com?subject=Chai%20Research%20Paper%20Enquiry) or Discord.
### Acknowledgements
This project would not have been possible without the support from members of [Seamless Capital](https://www.seamless-capital.com/)
We thank the following authors from the [Machine Intelligence Laboratory](https://mi.eng.cam.ac.uk/) for their collaboration:
- [Vysas Raina](https://www.linkedin.com/in/vyas-raina-71b226152/)
- [Adian Liusie](https://www.linkedin.com/in/adian-liusie-00b60511a/)
|
s-spektrum-m/FinSTA
|
s-spektrum-m
| 2023-03-13T08:05:12Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-03-13T03:51:10Z |
---
license: mit
author: Siddharth Mohanty
date: 3/12/2023
---
# FinSTA: Financial Share Tracking and Allocation
---
Distributed by Siddharth Mohanty under the MIT License 2023
|
danendra/Reinforce-Pixelcopter-PLE-v0
|
danendra
| 2023-03-13T08:04:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T06:09:23Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 24.90 +/- 19.14
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
dineshresearch/rl_course_vizdoom_health_gathering_supreme
|
dineshresearch
| 2023-03-13T07:57:05Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T07:56:58Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.18 +/- 4.79
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r dineshresearch/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Mehtap/base_09
|
Mehtap
| 2023-03-13T07:36:13Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"tr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-10T11:13:19Z |
---
language:
- tr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: base Turkish Whisper (bTW)
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. -->
# base Turkish Whisper (bTW)
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4238
- Wer: 0.9367
- Cer: 0.7611
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.6918 | 2.85 | 100 | 1.5023 | 0.7940 | 0.4289 |
| 0.6823 | 5.71 | 200 | 1.0475 | 0.8783 | 0.5573 |
| 0.4277 | 8.57 | 300 | 0.9944 | 0.8054 | 0.6120 |
| 0.2244 | 11.43 | 400 | 1.0460 | 0.6878 | 0.3825 |
| 0.1138 | 14.28 | 500 | 1.2059 | 0.7510 | 0.5020 |
| 0.0468 | 17.14 | 600 | 1.2180 | 1.1436 | 1.0719 |
| 0.0193 | 19.99 | 700 | 1.2801 | 1.1500 | 0.9344 |
| 0.0093 | 22.85 | 800 | 1.4574 | 0.9238 | 0.6799 |
| 0.0068 | 25.71 | 900 | 1.4137 | 0.9400 | 0.8128 |
| 0.0062 | 28.57 | 1000 | 1.4238 | 0.9367 | 0.7611 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Mehtap/base_07
|
Mehtap
| 2023-03-13T07:35:20Z | 85 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"tr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-03T09:43:39Z |
---
language:
- tr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: base Turkish Whisper (bTW)
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. -->
# base Turkish Whisper (bTW)
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1836
- Wer: 1.7109
- Cer: 1.2860
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.7878 | 4.74 | 100 | 1.4516 | 0.8560 | 0.5525 |
| 0.6701 | 9.51 | 200 | 0.9194 | 0.8543 | 0.6112 |
| 0.3364 | 14.28 | 300 | 0.8871 | 0.7415 | 0.4992 |
| 0.1228 | 19.05 | 400 | 0.9671 | 0.9052 | 0.6678 |
| 0.0355 | 23.78 | 500 | 1.0515 | 0.8961 | 0.6208 |
| 0.0148 | 28.55 | 600 | 1.0684 | 0.6644 | 0.3694 |
| 0.0056 | 33.32 | 700 | 1.1488 | 1.3315 | 0.8732 |
| 0.0041 | 38.09 | 800 | 1.1700 | 1.7415 | 1.1934 |
| 0.0034 | 42.83 | 900 | 1.1801 | 1.7745 | 1.2643 |
| 0.0032 | 47.6 | 1000 | 1.1836 | 1.7109 | 1.2860 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Mehtap/base_06
|
Mehtap
| 2023-03-13T07:34:43Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"tr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-02T11:46:36Z |
---
language:
- tr
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: base Turkish Whisper (bTW)
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. -->
# base Turkish Whisper (bTW)
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9500
- Wer: 2.1895
- Cer: 1.3548
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.7116 | 5.53 | 100 | 1.9115 | 1.1785 | 0.6901 |
| 0.6101 | 11.11 | 200 | 1.5123 | 1.1039 | 0.6221 |
| 0.2376 | 16.64 | 300 | 1.5636 | 0.9817 | 0.6448 |
| 0.0591 | 22.21 | 400 | 1.7179 | 2.2005 | 1.3384 |
| 0.0177 | 27.75 | 500 | 1.8454 | 1.9205 | 1.2140 |
| 0.0096 | 33.32 | 600 | 1.8529 | 1.2983 | 0.7777 |
| 0.0048 | 38.85 | 700 | 1.9306 | 2.3411 | 1.4385 |
| 0.0032 | 44.43 | 800 | 1.9388 | 1.9523 | 1.2705 |
| 0.0028 | 49.96 | 900 | 1.9472 | 1.8655 | 1.2023 |
| 0.0026 | 55.53 | 1000 | 1.9500 | 2.1895 | 1.3548 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
|
reachrkr/ppo-SnowballTarget
|
reachrkr
| 2023-03-13T07:15:38Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-13T07:15:32Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: reachrkr/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
dineshresearch/ppo-LunarLander-v3
|
dineshresearch
| 2023-03-13T07:12:59Z | 3 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-06T14:37:14Z |
---
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: -121.76 +/- 94.73
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': 100000
'learning_rate': 0.00025
'num_envs': 4
'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': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'dineshresearch/ppo-LunarLander-v3'
'batch_size': 512
'minibatch_size': 128}
```
|
dineshresearch/ppo-LunarLander-v2
|
dineshresearch
| 2023-03-13T07:10:35Z | 2 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-06T12:41:43Z |
---
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: -119.34 +/- 68.97
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': 50000
'learning_rate': 0.00025
'num_envs': 4
'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': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'dineshresearch/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
dineshresearch/ppo-SoccerTwos3
|
dineshresearch
| 2023-03-13T06:29:39Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-13T06:29:31Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: dineshresearch/ppo-SoccerTwos3
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
alvarez/Reinforce_Pixelcopter_001
|
alvarez
| 2023-03-13T06:16:27Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T06:09:02Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce_Pixelcopter_001
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 12.20 +/- 8.57
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
sd99/Pixelcopter-PLE-v1
|
sd99
| 2023-03-13T06:14:46Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T06:14:42Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 24.60 +/- 15.76
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Carlosrelao/q-Taxi-v3
|
Carlosrelao
| 2023-03-13T06:10:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T06:10:41Z |
---
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: 7.48 +/- 2.66
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="Carlosrelao/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"])
```
|
Carlosrelao/q-FrozenLake-v1-4x4-noSlippery
|
Carlosrelao
| 2023-03-13T06:06:13Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T06:06:04Z |
---
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="Carlosrelao/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"])
```
|
Huffon/paraphrase-multilingual-mpnet-base-v2-512
|
Huffon
| 2023-03-13T05:42:17Z | 8 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-03-08T07:43:51Z |
---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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.
## 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('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
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('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
# 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, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
juansebashr/Reinforce-Pixelcopter-PLE-v0
|
juansebashr
| 2023-03-13T05:38:44Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T03:53:43Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 41.10 +/- 23.39
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
syaimu/7th_JP_test
|
syaimu
| 2023-03-13T05:32:42Z | 0 | 78 | null |
[
"license:other",
"region:us"
] | null | 2023-03-13T05:13:08Z |
---
license: other
---
the skin color of Japanese people.
<img src="https://i.imgur.com/sJjPD5h.jpg" width="1700" height="">
|
EnD-Diffusers/PunkedMerge
|
EnD-Diffusers
| 2023-03-13T05:31:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable diffusion",
"duskfallcrew",
"comic book style",
"anime",
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-03-13T05:21:06Z |
---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
tags:
- stable diffusion
- duskfallcrew
- comic book style
- anime
---
Trying to push more of our content that is merges to HF.
If you want to see what this is :
https://civitai.com/models/5517/duskfalls-punked-merge-mix
I'll get the sample pics running and more details when i have time!
Duskfall's Punked Merge Mix
If you want to donate towards costs and don't want to subscribe:
https://ko-fi.com/DUSKFALLcrew
If you want to monthly support the EARTH & DUSK media projects and not just AI:
https://www.patreon.com/earthndusk
Discord: https://discord.gg/Da7s8d3KJ7
Source Merges:
Duskfall AI
Duskfall Portal Crew
Duskfall's Dissociated Open Ended Journey
Elldreth's Lucid Mix
Synthwave Punk
Prompt Hero's OpenJourney
Yes if you use lisdusk 1 and 2, kairowez and duskypie you WILL get cat people. Less of our art, more cat people.
As this has DREAMLIKE INCLUDED, be aware you cannot commercialize this. Except y'know generated images.
See Elldreth's Lucid mix and Dreamlike 1.0
Lisc listed here: https://civitai.com/models/1274/dreamlike-diffusion-10
|
Rishu115/mlm-bert-train_finalTraining
|
Rishu115
| 2023-03-13T05:22:50Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"tf",
"bert",
"generated_from_keras_callback",
"region:us"
] | null | 2023-03-11T11:37:46Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: Rishu115/mlm-bert-train_finalTraining
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. -->
# Rishu115/mlm-bert-train_finalTraining
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2958
- Validation Loss: 1.1886
- Epoch: 6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 47396, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.2956 | 1.1881 | 0 |
| 1.2949 | 1.1895 | 1 |
| 1.2951 | 1.1890 | 2 |
| 1.2952 | 1.1902 | 3 |
| 1.2950 | 1.1869 | 4 |
| 1.2957 | 1.1867 | 5 |
| 1.2958 | 1.1886 | 6 |
### Framework versions
- Transformers 4.23.1
- TensorFlow 2.10.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
chocoyj/distilbert-base-uncased-finetuned-emotion
|
chocoyj
| 2023-03-13T04:54:51Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-13T04:44:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9275
- name: F1
type: f1
value: 0.9276043877262424
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2154
- Accuracy: 0.9275
- F1: 0.9276
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8125 | 1.0 | 250 | 0.3089 | 0.9055 | 0.9030 |
| 0.2492 | 2.0 | 500 | 0.2154 | 0.9275 | 0.9276 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
jilsa212/output2
|
jilsa212
| 2023-03-13T04:34:13Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-19T20:11:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: output2
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. -->
# output2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1000
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4-use-violation
|
dshin
| 2023-03-13T04:22:26Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:22:00Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpwgnn_c73/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpwgnn_c73/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpwgnn_c73/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-f-batch-size-8-epoch-4
|
dshin
| 2023-03-13T04:22:16Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:21:51Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpgzelo33q/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-4")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpgzelo33q/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-4")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpgzelo33q/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-4")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-h-batch-size-8-epoch-4-use-violation
|
dshin
| 2023-03-13T04:22:00Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:21:34Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp9l_sekkg/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-4-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp9l_sekkg/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-4-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp9l_sekkg/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-4-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3-use-violation
|
dshin
| 2023-03-13T04:21:54Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:21:25Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpfs6upoqk/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpfs6upoqk/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpfs6upoqk/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4
|
dshin
| 2023-03-13T04:21:46Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:21:21Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp7i_j0cj5/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp7i_j0cj5/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp7i_j0cj5/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-4")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-f-batch-size-8-epoch-3
|
dshin
| 2023-03-13T04:21:45Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:21:19Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp4r5qkujd/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-3")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp4r5qkujd/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-3")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp4r5qkujd/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-3")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-a-batch-size-8-epoch-4
|
dshin
| 2023-03-13T04:21:43Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:21:18Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpr3qr4507/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-4")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpr3qr4507/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-4")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpr3qr4507/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-4")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3
|
dshin
| 2023-03-13T04:21:15Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:20:49Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp2hrh7rtc/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp2hrh7rtc/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp2hrh7rtc/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-3")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2-use-violation
|
dshin
| 2023-03-13T04:20:54Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:20:29Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpy5lmo_rk/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpy5lmo_rk/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpy5lmo_rk/dshin/flan-t5-ppo-user-h-batch-size-8-epoch-2-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1-use-violation
|
dshin
| 2023-03-13T04:20:46Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:19:46Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpozmzub77/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpozmzub77/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpozmzub77/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-f-batch-size-8-epoch-1
|
dshin
| 2023-03-13T04:20:42Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:19:45Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpf8uphmjt/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-1")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpf8uphmjt/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-1")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpf8uphmjt/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-1")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-a-batch-size-8-epoch-2
|
dshin
| 2023-03-13T04:20:40Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:20:14Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpr8z0sw2q/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-2")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpr8z0sw2q/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-2")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpr8z0sw2q/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-2")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1
|
dshin
| 2023-03-13T04:20:09Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:19:43Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp2rtc3oyu/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp2rtc3oyu/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp2rtc3oyu/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-1")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-f-batch-size-8-epoch-0-use-violation
|
dshin
| 2023-03-13T04:19:37Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:19:08Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmpl2v6uzne/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-0-use-violation")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmpl2v6uzne/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-0-use-violation")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpl2v6uzne/dshin/flan-t5-ppo-user-f-batch-size-8-epoch-0-use-violation")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-e-batch-size-8-epoch-0
|
dshin
| 2023-03-13T04:19:36Z | 46 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:19:07Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp374vyfy5/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-0")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp374vyfy5/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-0")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp374vyfy5/dshin/flan-t5-ppo-user-e-batch-size-8-epoch-0")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
dshin/flan-t5-ppo-user-a-batch-size-8-epoch-0
|
dshin
| 2023-03-13T04:19:35Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:19:08Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="dshin//tmp/tmp29t_zb54/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-0")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp29t_zb54/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-0")
model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp29t_zb54/dshin/flan-t5-ppo-user-a-batch-size-8-epoch-0")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
slopezay/a2c-PandaReachDense-v2
|
slopezay
| 2023-03-13T04:11:40Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T04:09:00Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.38 +/- 0.76
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
juansebashr/dqn-SpaceInvadersNoFrameskip-v4
|
juansebashr
| 2023-03-13T03:38:58Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T03:36:47Z |
---
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: 686.50 +/- 256.90
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga juansebashr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga juansebashr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga juansebashr
```
## 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)])
```
|
avoroshilov/ppo-SoccerTwos
|
avoroshilov
| 2023-03-13T03:35:05Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-03-12T13:19:04Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: avoroshilov/ppo-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
ToluClassics/extractive_reader_nq
|
ToluClassics
| 2023-03-13T03:27:00Z | 121 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-12T13:45:08Z |
---
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: extractive_reader_nq
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. -->
# extractive_reader_nq
This model is a fine-tuned version of [ToluClassics/extractive_reader_nq](https://huggingface.co/ToluClassics/extractive_reader_nq) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
juansebashr/Reinforce-CartPole-v1
|
juansebashr
| 2023-03-13T03:01:49Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T03:01:40Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ShreyasM/PixelCopter
|
ShreyasM
| 2023-03-13T02:48:23Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-13T02:06:51Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.40 +/- 12.32
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
WinterMute011/my_awesome_qa_model
|
WinterMute011
| 2023-03-13T02:40:53Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-13T01:01:03Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: WinterMute011/my_awesome_qa_model
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. -->
# WinterMute011/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6512
- Validation Loss: 1.8806
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5835 | 2.3688 | 0 |
| 1.9443 | 1.8806 | 1 |
| 1.6512 | 1.8806 | 2 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.12.0-rc1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
KonghaYao/MagicPrompt_SD_V1
|
KonghaYao
| 2023-03-13T02:23:26Z | 0 | 2 | null |
[
"paddlepaddle",
"code",
"text-generation",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2023-03-09T12:53:02Z |
---
license: cc-by-sa-4.0
language:
- en
pipeline_tag: text-generation
tags:
- code
---
# MagicPrompt_SD_V1
This is a Prompt Generator likes [Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion)!
But I wash the origin prompts data, and trains a powerful model to generate prompt for [้ญๅฏผ็ปช่ฎบ](https://magic-tag.netlify.app/)
It's using Paddle to handle the training and other things. Not PyTorch or Tensorsflow.
There's the result I get form this model:
- You can use CPU to run the model! But GPU 10x faster then CPU ๐.
- CPU (about 300ms/per ) | GPU (about 90ms/per ๐ ) V2-10 Model
- You can add some change easier passing some params.
## ๐ Using example is here
[้ฃๆกจไปๅบ](https://aistudio.baidu.com/aistudio/projectdetail/5116158?contributionType=1)
You can wrapper a FastAPI or Flask to easily deploy it to your server
|
williamberman/controlnet-model-3-12-learning-rates
|
williamberman
| 2023-03-13T01:43:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"region:us"
] | null | 2023-03-13T01:31:45Z |
https://wandb.ai/williamberman/controlnet-model-3-11-mixed-precision/runs/b2mfgr68
|
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