modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
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|---|---|---|---|---|---|---|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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"BertForSequenceClassification"
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| 33 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.71 +/- 21.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"BertModel"
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}
| 4 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dcduplooy/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"])
```
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"BertForQuestionAnswering"
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}
}
| 3 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: Schoolar/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"RobertaForQuestionAnswering"
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| 2 | null |
---
license: openrail
---
Epoch 80 (overtrained) from training a dataset kindly provided by @pashahlis; see [https://huggingface.co/damian0815/pashahlis-val-test-1e-6-ep30](https://huggingface.co/damian0815/pashahlis-val-test-1e-6-ep30) for more information.
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"RobertaForSequenceClassification"
],
"model_type": "roberta",
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}
| 24 | null |
---
language:
- tr
tags:
- mdeberta
license: cc-by-nc-sa-4.0
---
# deprem-mdeberta-ner
Fine-tuned mDeBERTa model for Turkish named entity recognition detecting PERSON, ADDRESS, CITY, STATUS of the tweets calling for help in the earthquake disaster. The model was trained using the tweets posted in the first 12 hours of the 2023 Turkey-Syria Earthquake.
The dataset and other details can be found at:
https://github.com/avaapm/deprem
### BibTeX entry and citation info
```bibtex
@misc{toraman2023earthquake,
doi = {10.48550/ARXIV.2302.13403},
url = {https://arxiv.org/abs/2302.13403},
author = {Toraman, Cagri and Kucukkaya, Izzet Emre and Ozcelik, Oguzhan and Sahin, Umitcan},
keywords = {Social and Information Networks (cs.SI), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
AnonymousSub/unsup-consert-emanuals
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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| 2 | 2023-02-26T20:32:05Z |
---
tags:
- adapterhub:sentiment/amazon
- adapter-transformers
- bert
datasets:
- amazon
---
# Adapter `domadapter/ts_dt_MR_apparel` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/ts_dt_MR_apparel", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Anthos23/FS-distilroberta-fine-tuned
|
[
"pytorch",
"roberta",
"text-classification",
"transformers",
"has_space"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 33 | null |
---
tags:
- bert
- adapterhub:sentiment/amazon
- adapter-transformers
datasets:
- amazon
---
# Adapter `domadapter/domain_only_apparel_camera_photo` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/domain_only_apparel_camera_photo", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Anubhav23/indianlegal
|
[] | null |
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| 0 | null |
---
tags:
- bert
- adapterhub:sentiment/amazon
- adapter-transformers
datasets:
- amazon
---
# Adapter `domadapter/domain_only_books_MR` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/domain_only_books_MR", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Anubhav23/model_name
|
[] | null |
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| 0 | null |
---
tags:
- bert
- adapterhub:sentiment/amazon
- adapter-transformers
datasets:
- amazon
---
# Adapter `domadapter/domain_only_camera_photo_apparel` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [sentiment/amazon](https://adapterhub.ml/explore/sentiment/amazon/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("domadapter/domain_only_camera_photo_apparel", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
asaakyan/mbart-poetic-all
|
[] | null |
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| 0 | null |
monot5-3b-inpars-v2-webis-touche2020-promptagator is a monoT5-3B model finetuned on Touche-2020 synthetic data generated by [InPars](https://github.com/zetaalphavector/inPars).
Currently, if you use this tool you can cite the original [InPars paper published at SIGIR](https://dl.acm.org/doi/10.1145/3477495.3531863) or [InPars-v2](https://arxiv.org/abs/2301.01820).
```
@inproceedings{inpars,
author = {Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Nogueira, Rodrigo},
title = {{InPars}: Unsupervised Dataset Generation for Information Retrieval},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531863},
doi = {10.1145/3477495.3531863},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2387–2392},
numpages = {6},
keywords = {generative models, large language models, question generation, synthetic datasets, few-shot models, multi-stage ranking},
location = {Madrid, Spain},
series = {SIGIR '22}
}
```
```
@misc{inparsv2,
doi = {10.48550/ARXIV.2301.01820},
url = {https://arxiv.org/abs/2301.01820},
author = {Jeronymo, Vitor and Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Lotufo, Roberto and Zavrel, Jakub and Nogueira, Rodrigo},
title = {{InPars-v2}: Large Language Models as Efficient Dataset Generators for Information Retrieval},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
AvatarXD/DialoGPT-medium-Blitzo
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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| 14 | 2023-02-27T00:15:05Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: zoalearn2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.809602677822113
---
# zoalearn2
zoalearn2 is [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) fine-tuned to classify 32 of the most popular coral speices.
|
Ayham/roberta_gpt2_summarization_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
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| 6 | null |
---
license: creativeml-openrail-m
language:
- ja
- en
tags:
- stable-diffusion
- text-to-image
thumbnail: "https://huggingface.co/NegiInNattoMaki/Nabylon/resolve/main/logo1.jpg"
---
# Nabylon
Nabylon is a mixed model based on AbyssOrangeMix2, LonganMix and others with some modifications.<br/>
(Nabylon = **N**attoMaki's merged model based on **Aby**ssOrangeMix2 and **Lon**ganMix)
The model was created by block-by-block weighted merging,
and the weights were designed to provide
both a moderate anime-like rendering of the persons and
an excellent rendering of the background.
Many thanks to those who have published the base model,
as well as its ancestor models.
#### (JP)
NabylonはAbyssOrangeMix2とLonganMix他をベースとして、いくつかの変更を加えた混合モデルです。<br/>
(Nabylon = **N**attoMaki's merged model based on **Aby**ssOrangeMix2 and **Lon**ganMix)
層別マージにより作成されており、人物の適度なアニメ感と精緻な背景が両立するように重みが設計されています。
ベースとなったモデル、およびその祖先となるモデルを提供してくださった皆様に感謝を。
- AbyssOrangeMix2<br/>https://huggingface.co/WarriorMama777/OrangeMixs
- LonganMix<br/>https://huggingface.co/Hemlok/LonganMix
- and others
## Nabylon v1.3
Incorporated the features of v1.2, but is tuned in a different policy.
The main focus of this model is on scene depth, making it easier to create wider shots.
The output of this model will be more illustrative, with a slight reduction in shadow rendering, and will focus on general and standard expressions.
Each version of Nabylon have its own characteristics, and you can switch them depending on what you want to illustrate.
| Version | Features |
| ---- | ---- |
| v1.3 | Wider composition, scene depth, more illustration-oriented |
| v1.2 | Focus to person, detailed backgrounds, slightly illustration-oriented |
| v1.0 | Focus to person, highest detailed backgrounds |
#### (JP)
v1.2の特徴を採り入れつつも、異なる方向にチューニングしたモデルです。
奥行きのある表現を重視し、広めの構図が出やすくなっています。
描写はイラスト寄りで陰影の描画はやや抑えつつ、親しみやすい表現を志向しています。
各バージョンに特徴がありますので、題材によって使い分けてください。
| バージョン | 特徴 |
| ---- | ---- |
| v1.3 | 広めの構図、奥行き感、イラスト寄り |
| v1.2 | 人物中心の構図、高精細な背景、ややイラスト寄り |
| v1.0 | 人物中心の構図、最も高精細な背景 |
## Nabylon v1.2
Nabylon v1.2 is based on v1.0 with a few modifications to increase the fidelity of the prompt and to draw people in a slightly more familiar way.
Throughout these adjustments, special care has been taken to maintain the accurate rendering of the background.
(v1.1 is missing due to failed adjustments)
There is a slight difference in the amount of structural breaks and the level of rendering detail and shading, so you can choose the appropriate model to suit your preference. It is recommended that you try switching between the two models.
#### (JP)
v1.0をベースとして、promptへの忠実性をより高め、人物の描き方がややメジャー寄りになるよう調整を加えたモデルです。背景の精緻さは維持しています。
(v1.1は調整に失敗したため欠番です)
破綻の出方や細部の描き込み・陰影にわずかに差がありますので、お好みで選択してください。両方のモデルを切り替えながら試すのもお薦めです。
# How to use
Download .ckpt and/or .safetensors and
place them to appropriate WebUI's model directory such as
`stable-diffusion-webui/models/Stable-diffusion`.
Any VAEs can be used; our recommendation is `sd-vae-ft-mse-original`.
Using "Deep Negative" is also recommended, but optional.
#### (JP)
.ckptまたは.safetensorsをダウンロードして
`stable-diffusion-webui/models/Stable-diffusion`
に配置してください。
VAEは好きなものを使ってください。
おすすめは `sd-vae-ft-mse-original` です。
Deep Negativeの使用を推奨しますが、必須ではありません。
- sd-vae-ft-mse-original<br/>https://huggingface.co/stabilityai/sd-vae-ft-mse-original/tree/main
- Deep Negative<br/>https://civitai.com/models/4629/deep-negative-v1x
# Samples (v1.3 / v1.2 / v1.0)
common parameters:
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate legs and hands and fingers
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Size: 960x960, Denoising strength: 0.45, Clip skip: 2, Hires upscale: 1.6, Hires upscaler: R-ESRGAN 4x+ Anime6B
---
**Positive** magnificent view, a girl wearing organza dress sitting on a cliff, small breasts, black hair and red eyes with highlights
---
**Positive** (hires, hdr:1.2), cinematic angle, depth of field, steampunk, close up to hands of girl. warm, dirty, spark. An extremely complex, precise, majestic, and beautiful (classical android is disassembled:1.4) and placed in a (dark factory:1.4). (The android is composed of:1.2) many gears, shafts, and mechanical parts. A human nekomimi girl (wearing coveralls:1.2) with a spanner in her hand, her red hair tied up and goggles on her head is assembling the android, and tools are placed all over the floor. The factory is dark, and (only her hand is impressively illuminated by a industrial lamp:1.6). (looking below:1.2), (small breasts:1.15), orange eyes
---
**Positive** (simple, deformed:1.0), (white background:1.2). miniature garden of small deformed (solo nekomimi girl wearing dirndl:1.2) hugging a (large sign:1.2) with the name of a company and many sitting cats, text, logo, nekomimi, gress, small breasts, shoes
Negative prompt: (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate legs and hands and fingers
---
**Positive** (hires, illustration, ray tracing:1.4). Inside a crystalline futuristic precision machine composed of regular orthogonal surfaces, stripes of laser light refracted and reflected carve beautiful geometric patterns. (illustration of an anime:1.3) glowing nekomimi hacker girl (wearing ribbon trimmed fluffy wrinkled very thin translucent PVC material miko:1.2) with shiny black long hair and clear eyes with highlights is incorporated as a component of the machine and sitting from side from behind, and (a cyborg device on her back connected to surrounding machines:1.4). small breasts, bare legs
---
**Positive** A mechanical sphere capsule includes a mechanical cyborg girl made with extremely detailed complex machine consisted with many gears and wheels, and the girl is in fetal position in the capsule, and the girl has Intricately knotted very long red hair, mechanical irises, foggy background
---
**Positive** hires, warm, bokeh. Flowers are placed in the center of a bright and atmospheric classical kitchen, and two blonde or brown hair aristocratic girls wearing (frilled white bib apron:1.2) over blue and white elegant and luxurious noble dress with elbow-length sleeves tied with strings and hair up using black ribbon and braided with ribbons are standing and cooking chocolate cake. The two girls are talking happily and smiling. (small breasts:1.15), (green eyes:1.2) with brilliant highlights, steam
---
**Positive** hires, flower garden with magnificent view, (2girls:1.2) wearing organza dress standing, small breasts, black and blonde hair and blue or red eyes with highlights
---
**Positive** Flowers are placed in the center of a bright and atmospheric atelier, and an aristocratic girl is sitting and facing to a mirror. Another servant girl is standing and tending to the noble girl's hair. The two girls are talking happily.
# Samples (v1.0 vs. v1.2)
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** hires, beautiful landscape from sky and mountain's peak appears through clouds and solo skinny nekomimi girl flying on the sky
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 2230501030, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x |
---
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** The battlefield where the urban warfare is taking place, and fires are rising from everywhere, and 2girls are fighting on the battlefield.
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 1512574150, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** Two nekomimi girls enjoy playing beach volleyball on a beach in the summer sun, and the game is heated and the girls are posing very dynamically, motion blur
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 1712193350, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** There's a reclusive girl in a cluttered room, frenzied and furious that she has over 300 followers.
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 3923096915, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** The old, atmospheric, lamp-lit apothecary is owned by a suspicious-looking girl in an expensive wizard's costume and wearing a (nekomimi hood:1.2) deeply, trying to sell an obviously dangerous medicine to a squirrel customer. The (squirrel offers her a gold coin:1.3). The apothecary is littered with many medicine bottles and laboratory equipment.
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 2317408102, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** hires, cinematic angle, very wide shot, In a dark and foggy cave, a girl is violently fighting a powerful demon in the distance. The girl is wearing extremely detailed majestic but practical white silver armor and holds a white silver sword and shield of the same design. The battle is fierce and sparkling, and the girl is in a dynamic posture.
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 708858631, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** A pilot girl sits in the cockpit of an intricately designed combat helicopter and controls the helicopter (from side:1.4). Around the girl are many meters and instruments showing the battle situation. Sparks are flying in the cockpit. The girl is wearing combat uniform and her face is illuminated by the light of the instruments, giving her a grim expression.
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 94156036, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
| v1.0 | v1.2 |
| ---- | ---- |
|  |  |
- **Positive** A girl in sophisticated winter clothes is waving her hand loudly, calling for people. In one hand she holds a can of coffee.
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), inaccurate hand and fingers, short legs
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 6, Seed: 1080308203, Size: 768x768, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
# Samples (v1.0)
- **Positive** depth of field, solo skinny nekomimi matured girl wearing dirndl standing and leaning forward from above in the distance in old castle ruins terrace, complex blue hair with hair ribbon with purple color tip, yellow eyes, soft smile, castle ruins landscape
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4)
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 8.5, Seed: 1021013292, Size: 640x640, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
- **Positive** depth of field, solo skinny nekomimi matured girl wearing dirndl sitting and dangling legs from above in the distance in old town ruins terrace, complex red color hair with hair ribbon, soft smile, city ruins landscape
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4)
- Steps: 35, Sampler: DPM++ SDE Karras, CFG scale: 4.5, Seed: 1127977195, Size: 640x640, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
- **Positive** hires, cinematic angle, nekomimi 3girls standing on water in the distance in beautiful seaside, glistening light of waves, moonlight
- **Negative** Negative prompt: (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4)
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 8.5, Seed: 1047767227, Size: 640x640, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
- **Positive** deformed chibi nekomimi girl dancing in extremely detailed dollhouse, narrow depth of field, bokeh
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4)
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 8.5, Seed: 2893868374, Size: 640x640, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
- **Positive** hires, field of view, extremely detailed dynamic view from the cliff in summer and (very wide shot:1.2) of solo skinny nekomimi girl (in the distance:1.2) with dynamic pose
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4)
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 8.5, Seed: 89008205, Size: 640x640, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
- **Positive** sci-fi, gemological color, hires, cinematic angle, solo nekomimi engineer girl working at (extremely detailed futuristic engine room of space ship:1.2) using many tools
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4)
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 8.5, Seed: 3595306620, Size: 640x640, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
- **Positive** cinematic angle, warm light, rain, solo nekomimi girl looking at viewer and walking through night europian medieval city holding classical umbrella with platinum hair and green eyes, focus to face, narrow wet cobblestone, reflections of candle light, splashing water, many peoples walking or standing, carriages, bokeh
- **Negative** (NG_DeepNegative_V1_75T:1.4), (worst quality, low quality, logo, text, watermark, username, male:1.4), cars, asian characters
- Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 8.5, Seed: 974591286, Size: 640x640, Denoising strength: 0.55, Clip skip: 2, Hires upscale: 1.8, Hires upscaler: ESRGAN_4x
---
license: creativeml-openrail-m
---
|
Ayham/robertagpt2_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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}
| 4 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 1009.50 +/- 383.92
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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 macb -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 macb -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 macb
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Ayran/DialoGPT-small-harry-potter-1-through-3
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
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},
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},
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}
}
}
| 12 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: LucaReggiani/t5-small-11nlpfinalproject11-xsum
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. -->
# LucaReggiani/t5-small-11nlpfinalproject11-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0366
- Validation Loss: 2.9572
- Train Rouge1: 23.0678
- Train Rouge2: 4.8820
- Train Rougel: 18.2146
- Train Rougelsum: 18.0961
- Train Gen Len: 18.73
- Epoch: 9
## 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': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.8944 | 3.2778 | 18.4040 | 2.9447 | 14.7588 | 14.8854 | 18.71 | 0 |
| 3.5118 | 3.1285 | 21.0571 | 4.0329 | 16.5313 | 16.5872 | 18.17 | 1 |
| 3.3821 | 3.0720 | 21.2823 | 4.1817 | 16.3643 | 16.3809 | 18.38 | 2 |
| 3.3099 | 3.0368 | 21.3656 | 4.0228 | 16.6094 | 16.5866 | 18.5 | 3 |
| 3.2464 | 3.0117 | 21.6946 | 4.2746 | 16.7999 | 16.7907 | 18.68 | 4 |
| 3.2081 | 2.9932 | 23.3785 | 5.3998 | 18.5529 | 18.5770 | 18.6 | 5 |
| 3.1603 | 2.9809 | 23.2570 | 5.4772 | 18.6532 | 18.6172 | 18.55 | 6 |
| 3.1169 | 2.9719 | 23.0897 | 4.7919 | 18.2567 | 18.1743 | 18.59 | 7 |
| 3.0696 | 2.9681 | 22.5213 | 4.9309 | 17.9595 | 17.8530 | 18.6 | 8 |
| 3.0366 | 2.9572 | 23.0678 | 4.8820 | 18.2146 | 18.0961 | 18.73 | 9 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Ayta/Haha
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/12170/sento-isuzu
|
AyushPJ/ai-club-inductions-21-nlp-ALBERT
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
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},
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},
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}
}
| 8 | null |
---
license: creativeml-openrail-m
---
<details>
<summary>Change logs</summary>
- 2023/03/02: Uploaded [a new version](https://huggingface.co/p1atdev/badquality/blob/f10deeff7b9f99a99ba9a94002dbb011e146fe86/badquality.pt) for WD 1.5 beta 2.
- 2023/02/27: Uploaded [the first version](https://huggingface.co/p1atdev/badquality/blob/813b5e7a81862e0594333cc3fd6e2a8e0092b684/badquality.pt) for WD 1.5 beta epoch1+.
</details>
<h1 class="mt-4">badquality</h1>
Negative prompt embedding for WD1.5 beta (beta 2). (Be careful not to use in positive prompt!)
Download [badquality.pt](https://huggingface.co/p1atdev/badquality/blob/main/badquality.pt) and put it into the embeddings folder.
```
masterpiece, best quality, exceptional, best quality, 1girl, loli, red hair, cat ears, dress, from above, looking at viewer, beautiful detailed
Negative prompt: badquality
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 3654617288, Size: 512x768, Model hash: 4bf1855f4d, Model: wd-1-5-beta2-fp16, Clip skip: 2
```
with the aesthetic version,
```
masterpiece, best quality, exceptional, 1girl, cat ears, blue hair, parted bangs, high ponytail, white shirt, dress shirt, looking at viewer,
Negative prompt: badquality
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Seed: 2930971727, Size: 512x768, Model hash: 96fc8b5de4, Model: wd-1-5-beta2-aesthetic-fp16, Clip skip: 2
```
|
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad
|
[
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
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"ElectraForQuestionAnswering"
],
"model_type": "electra",
"task_specific_params": {
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}
}
| 12 | null |
Access to model DeLiss/RitaRossweisse is restricted and you are not in the authorized list. Visit https://huggingface.co/DeLiss/RitaRossweisse to ask for access.
|
AyushPJ/test-squad-trained-finetuned-squad
|
[
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
}
| 8 | null |
---
language:
- ko
library_name: doctr
---
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
Azaghast/DistilBERT-SCP-Class-Classification
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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}
}
| 42 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GGunjan/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"])
```
|
BE/demo-sentiment2021
|
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}
| 0 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1777.32 +/- 23.42
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
...
```
|
BOON/electra_qa
|
[] | null |
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}
}
| 0 | null |
---
pipeline_tag: conversational
language:
- ko
tags:
- conversational
---
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("keonju/chat_bot")
model = AutoModelForCausalLM.from_pretrained("keonju/chat_bot")
# Let's chat for 5 lines
for step in range(5):
message = input("MESSAGE: ")
if message in ["", "q"]: # if the user doesn't wanna talk
break
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
if (trained):
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature = 0.8,
)
else:
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3
)
# pretty print last ouput tokens from bot
print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
|
BSC-LT/gpt2-large-bne
|
[
"pytorch",
"gpt2",
"text-generation",
"es",
"dataset:bne",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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}
}
| 11 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 225.11 +/- 71.22
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BSC-LT/roberta-base-biomedical-clinical-es
|
[
"pytorch",
"roberta",
"fill-mask",
"es",
"arxiv:2109.03570",
"arxiv:2109.07765",
"transformers",
"biomedical",
"clinical",
"spanish",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 27 | null |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).<br>
- Provide the model to an app such as Mochi Diffusion [Github](https://github.com/godly-devotion/MochiDiffusion) - [Discord](https://discord.gg/x2kartzxGv) to generate images.<br>
- `split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
- `original` version is only compatible with CPU & GPU option.<br>
- Custom resolution versions are tagged accordingly.<br>
- `vae` tagged files have a vae embedded into the model.<br>
- Descriptions are posted as-is from original model source. Not all features and/or results may be available in CoreML format.<br>
- This model was converted with `vae-encoder` for i2i.
# Note: Some models do not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
# MODELNAME:
Source(s): [Hugging Face](https://huggingface.co/Ayoni) - [CivitAI](https://civitai.com/models/4550/ayonimix)
About this version
Very slightly changed one of the merges and clipfixed the final result. The outputs should be more accurate to what is being prompted as a result of the clipfix.
|
BSC-LT/roberta-base-bne
|
[
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
| 594 | null |
This is just a copied checkpoint from TuneAVideo. The only difference is in the model_index.json where UNet3DCondition model is loaded from diffusers. Made this for the port I'm working on.
|
BSC-LT/roberta-large-bne-capitel-ner
|
[
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
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"max_length": null,
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}
}
}
| 5 | 2023-02-27T06:15:05Z |
---
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: -2.65 +/- 0.63
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
...
```
|
BSen/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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| 4 | null |
---
license: creativeml-openrail-m
---
# Okingjo's Multi-identifier LORAs
I will share most of my LORA model with multiple identifier here. by saying "multiple", one charater with two or more costumes are stored within the model.
Not only will the LORA model will be post here, training setups and tips will also be shared.
I`m still in the state of learning, so any comments/feedbacks are welcom!
# some tips during trainig
## general idea
The way to have multiple identifiers within one Lora is by using captioning. Since most auto captioning of an anime character starts with "1girl/boy", the second prompt will be used as the triggering word, i.e. the prompt to let the AI to excite the desired costume.
Captioning the images properly and put them into different folders respectively, just as the Kohya_SS SD trainier docs says, and you are good to go.
## captioning tips
Automatic captioning with manual tweak, minimizing the overlap between each identifier.
# Charater from Genshin
## Ningguang/凝光
### Brief intro
LORA of Ningguang, with two costumes in game. civitAI page [Download](https://civitai.com/models/8546/ningguang)
### Training dataset
#### Default costume
72 images in total, in folder "30_Ningguang"
* 36 normal illustrations
* 15 normal 360 3D model snapshots
* 5 nude illustrations
* 16 nude 360 3D model snapshots
#### Orchid costume
43 images in total, in folder "20_NingguangOrchid"
* 11 normal illustrations
* 15 normal 360 3D model snapshots
* 2 nude illustrations
* 15 nude 360 3D model snapshots
### Captioning
WD14 captioning instead of the deepdanbooru caption was used, since the former one will not crop/resize the images. Threshold are usually set to 0.75-0.8. since I don't like to have a very long and sometimes inaccurate caption for my training data. After captionin is done, I added "ningguang \ \(genshin impact\ \)" after "1girl" to every caption file of the default costume, and "ningguang \ \(orchid's evening gown\ \) \ \(genshin impact\ \)" to the orchid costume. Some of the caption files were empty so I have to manually type the words.
### Training setup
Trained with Kohya_SS stable diffusion trainer Base model was [Anything V3.0 full](https://huggingface.co/Linaqruf/anything-v3.0/blob/main/anything-v3-fp32-pruned.safetensors) Trainig process consist of two phases. The first one with default parameters of:
* learning_rate: 0.0001
* text_encoder_lr: 5e-5
* unet_lr: 0.0001 and 6 epoch,
After phase 1, choose the one with the best result (a little bit underfitting, no over fitting, and the two costume are seperated), which is the 6th one. Then trained with 1/10 of the original LR for another 7 epochs.
### Result
## Barbara/芭芭拉
### Brief intro
LORA of Barbara, with two costumes in game.
The filesize of the model has been decreased to 1/4, i.e. 2 identifiers in a 64 size (was 1 identifier per 128). On one hand, saving storasge; on the other hand, fewer room for char with lower priority. in summary, less is better!
### Training dataset
#### Default costume
164 images in total, in folder "10_barbara_(genshin_impact) 1girl"
* 104 illustrations, bothSFW and NSFW, handpicked to ensure best quality
* 30 normal 360 3D model snapshots
* 30 nude 360 3D model snapshots
#### Summertime swimsuit
94 imges in total, in folder "16_barbara_(summertime_sparkle)_(genshin_impact) 1girl"
* 64 illustrations, bothSFW and NSFW, handpicked to ensure best quality
* 30 normal 360 3D model snapshots
### Captioning
It was the first time that the standard Danbooru style prompt was used for captioning. "barbara_(genshin_impact)" and "barbara_(summertime_sparkle)_(genshin_impact)" were added to each costume respectively.
### Training setup
Defalut LR fo 4 epochs, then 1/10 default LR for another 8 epochs.
Trainig basing on anything v3.
Total steps is: (4+8)x(164x10+94x16)=37,728
### results
# Charater from Honkai impact
## Elysia/爱莉希雅-V2
### Brief intro
LORA of Elysia, with 5 costumes in game. civitAI page [Download](https://civitai.com/models/14616)
### Training dataset
#### Default costume/Miss Pink Elf
75 images in total, in folder "14_Elysia (miss pink elf) 1girl"
* 45 normal illustrations, non-nude
* 30 normal 360 3D model snapshots
#### Default herrscher costume/herrscher of human:ego
129 images in total, in folder "11_elysia (herrscher of humanego) 1girl"
* 39 normal illustrations, included few NSFW, not completely nude though
* 90 normal 360 3D model snapshots
#### Miad costume
75 images in total, in folder “16_Elysia-maid 1girl”
* 45 normal illustrations, non-nude
* 30 normal 360 3D model snapshots
#### swimsuit
80 images in total, in folder “14_elysia-swimsuit 1girl”
* 20 normal illustrations, non-nude
* 60 normal 360 3D model snapshots
In addition, I have also included 52 images with non-official costumes in a new folder "10_Elysia 1girl"
### Captioning
Captioning with WD1.4, with threshold 0.7.
After the auto captionin is done, I added "elysia \ \(miss pink elf\ \) \ \(honkai impact\ \)", "elysia \ \(herrscher of human:ego\ \) \ \(honkai impact\ \)", "Elysia-maid", "Elysia-swimsuit" and "1girl, elysia \ \(honkai impact\ \)" to the captioning respectively as identifiers. In addition, I also manually editted the captioning to make sure that there was no overlap of prompts between each concepts.
### Training setup
Trained with Kohya_SS stable diffusion trainer Base model was [Anything V3.0 full](https://huggingface.co/Linaqruf/anything-v3.0/blob/main/anything-v3-fp32-pruned.safetensors) Trainig process consist of two phases. The first one with default parameters of:
* Lion, Cosine with restarts
* learning_rate: 0.00003
* text_encoder_lr: 15e-6
* unet_lr: 0.00003
Trained for 10 epochs with upper parameters, feel underfit, thus trained another 2 epoch with 1/3 the initial LR.
### Result
|
Bagus/SER-LSSED
|
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| 0 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - lvhoang/out_dog
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
Bagus/ser-japanese
|
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| 0 | 2023-02-27T06:40:30Z |
---
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="ChhayaKumarDas/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"])
```
|
Bagus/wav2vec2-xlsr-greek-speech-emotion-recognition
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"el",
"dataset:aesdd",
"transformers",
"audio",
"audio-classification",
"speech",
"license:apache-2.0"
] |
audio-classification
|
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| 21 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-xlmberttest
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-xlmberttest
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9248
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0547 | 1.0 | 29 | 0.9248 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 1.10.3.dev0
- Tokenizers 0.12.1
|
Bakkes/BakkesModWiki
|
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### firuziat-style model trained by Falah.G.Salieh called in Arabic (فيروزيات)
## You can visit my blog: https://iraqprogrammer.wordpress.com/
## Or FB: https://web.facebook.com/falahgs
## Email: [email protected]
With Stable Diffusion, we can now create artificial intelligence art generation images using our trained
images. In this model, we can create images of new images and a real photo style called firuziat style with
Arabic artist singer Firuze images in Arabic (firuziat-style نمط فيروزيات )
Any prompt and add firuziat-style style word:
prompt:
a woman with black hair and earrings on her head looking off to the side with a black background and a black background, Anita Malfatti, promotional image,
dau-al-set,firuziat-style
Sample images of this concept with simple and easy prompts:
.png)
.png)
.png)
.png)
.png)
.png)
|
BalajiSathesh/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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| 8 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 245.61 +/- 25.63
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
...
```
|
Banshee/LukeSkywalker
|
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased
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-indobert-base-uncased
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9029
- Accuracy: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4716 | 1.0 | 1 | 5.9029 | 0.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
Banshee/dialoGPT-small-luke
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.05 +/- 18.26
name: mean_reward
verified: false
---
# **ppo** Agent playing **LunarLander-v2**
This is a trained model of a **ppo** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Barbarameerr/Barbara
|
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| 0 | null |
---
license: openrail
datasets:
- Ali-fb/dilbert-comic-sample-dataset
language:
- en
library_name: diffusers
tags:
- art
---
|
Barleysack/klue-roberta-LSTM
|
[
"pytorch",
"roberta",
"transformers"
] | null |
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| 6 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine-tuned-DatasetQAS-TYDI-QA-with-indobert-base-uncased
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-TYDI-QA-with-indobert-base-uncased
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0662
- Accuracy: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5427 | 1.0 | 1 | 6.0662 | 0.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
BatuhanYilmaz/bert-finetuned-mrpc
|
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| 0 | null |
---
model-index:
- name: medieval-it5-base
results: []
language:
- it
---
# medieval-it5-base
This model is a version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) fine-tuned on a dataset called [ita2medieval](https://huggingface.co/datasets/leobertolazzi/ita2medieval). The Dataset contains sentences from medieval italian along with paraphrases in contemporary italian (approximately 6.5k pairs in total).
The fine-tuning task is text-style-tansfer from contemporary to medieval italian.
## Using the model
```
from transformers import AutoTokenzier, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("leobertolazzi/medieval-it5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("leobertolazzi/medieval-it5-base")
```
Flax and Tensorflow versions of the model are also available:
```
from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration
model_flax = FlaxT5ForConditionalGeneration.from_pretrained("leobertolazzi/medieval-it5-base")
model_tf = TFT5ForConditionalGeneration.from_pretrained("leobertolazzi/medieval-it5-base")
```
## Training procedure
The code used for the fine-tuning is available in this [repo](https://github.com/leobertolazzi/medievalIT5)
## Intended uses & limitations
The biggest limitation for this project is the size of the ita2medieval dataset. In fact, it consists only of 6.5K sentence pairs whereas [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) has 220M parameters.
For this reason the results can be far from perfect, but some nice style translations can also be obtained.
It would be nice to expand ita2medieval with text and paraphrases from more medieval italian authors!
### Framework versions
- Transformers 4.26.0
- Tokenizers 0.13.2
|
BatuhanYilmaz/bert-finetuned-nerxD
|
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| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -313.16 +/- 58.73
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
...
```
|
BatuhanYilmaz/marian-finetuned-kde4-en-to-fr
|
[] | null |
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}
| 0 | 2023-02-27T07:52:53Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fine-tuned-DatasetQAS-Squad-ID-with-indobert-base-uncased
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-Squad-ID-with-indobert-base-uncased
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0963
- Accuracy: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.538 | 1.0 | 1 | 6.0963 | 0.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
BatuhanYilmaz/mt5-small-finetuned-amazonbooks-en-es
|
[] | null |
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| 0 | 2023-02-27T07:55:39Z |
---
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: 667.00 +/- 174.06
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 seungwoos -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 seungwoos -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 seungwoos
```
## 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)])
```
|
BeIR/query-gen-msmarco-t5-base-v1
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
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"num_beams": 4,
"prefix": "translate English to German: "
},
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"prefix": "translate English to French: "
},
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"prefix": "translate English to Romanian: "
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}
| 1,816 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="adhisetiawan/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"])
```
|
BeIR/query-gen-msmarco-t5-large-v1
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"prefix": "translate English to German: "
},
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"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
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}
| 1,225 | 2023-02-27T08:10:05Z |
---
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: 0.9296
---
<!-- 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: 0.2402
- Accuracy: 0.9296
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2312 | 1.0 | 1563 | 0.1943 | 0.9255 |
| 0.1535 | 2.0 | 3126 | 0.2402 | 0.9296 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Beatriz/model_name
|
[] | null |
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| 0 | 2023-02-27T08:12:32Z |
---
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="adhisetiawan/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"])
```
|
Beelow/model
|
[] | null |
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}
| 0 | 2023-02-27T08:15:43Z |
---
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: 76.01 +/- 68.86
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': 'KoRiF/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Begimay/Task
|
[] | null |
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}
}
| 0 | 2023-02-27T08:19:32Z |
---
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: -28.83 +/- 21.95
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'
'env_id': 'LunarLander-v2'
'learning_rate': 0.00025
'seed': 1
'total_timesteps': 1000000
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'num_envs': 16
'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': 'Your-Cheese/ppo-LunarLander-v2-Unit8'
'batch_size': 2048
'minibatch_size': 512}
```
|
BenDavis71/GPT-2-Finetuning-AIRaid
|
[
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
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| 10 | null |
Access to model L0SG/BigVGAN is restricted and you are not in the authorized list. Visit https://huggingface.co/L0SG/BigVGAN to ask for access.
|
BenQLange/HF_bot
|
[] | null |
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| 0 | null |
New version is available at: https://huggingface.co/NegiInNattoMaki/Nabylon
|
Benicio/t5-small-finetuned-en-to-ro
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-question-v-statement
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. -->
# bert-base-uncased-finetuned-question-v-statement
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0088
- Accuracy: 0.9990
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0035 | 1.0 | 7932 | 0.0078 | 0.9988 |
| 0.0018 | 2.0 | 15864 | 0.0088 | 0.9990 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Beri/legal-qa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 10 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-finetuned-question-v-statement-kaggle
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-question-v-statement-kaggle
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0066
- Accuracy: 0.9993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0025 | 1.0 | 7932 | 0.0093 | 0.9987 |
| 0.0054 | 2.0 | 15864 | 0.0056 | 0.9991 |
| 0.0027 | 3.0 | 23796 | 0.0066 | 0.9993 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
Berzemu/Coco
|
[] | null |
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}
| 0 | 2023-02-27T09:01:28Z |
---
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: 249.10 +/- 24.47
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
...
```
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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}
}
| 4 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.49 +/- 35.38
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
...
```
|
Bhumika/roberta-base-finetuned-sst2
|
[
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"model-index"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
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}
}
}
| 85 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8654677896653767
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1405
- F1: 0.8655
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2495 | 1.0 | 787 | 0.1764 | 0.8184 |
| 0.1299 | 2.0 | 1574 | 0.1427 | 0.8562 |
| 0.0771 | 3.0 | 2361 | 0.1405 | 0.8655 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Bhuvana/t5-base-spellchecker
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 93 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="clemdev2000/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"])
```
|
Biasface/DDDC2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 10 | null |
---
language:
- or
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Odia
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 or
type: mozilla-foundation/common_voice_11_0
config: or
split: test
args: or
metrics:
- name: Wer
type: wer
value: 43.356840620592386
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Odia
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 or dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4781
- Wer: 43.3568
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0628 | 12.01 | 250 | 0.2729 | 46.0649 |
| 0.0021 | 24.02 | 500 | 0.3792 | 59.7743 |
| 0.0004 | 37.01 | 750 | 0.4475 | 47.6728 |
| 0.0003 | 49.02 | 1000 | 0.4781 | 43.3568 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu102
- Datasets 2.10.0
- Tokenizers 0.13.2
|
BigBoy/model
|
[] | null |
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| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TaxiV3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.63
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="ChhayaKumarDas/TaxiV3", 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"])
```
|
BigSalmon/BlankSlots
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 4 | 2023-02-27T09:47:33Z |
---
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="clemdev2000/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"])
```
|
BigSalmon/InformalToFormalLincoln18
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Regression_albert_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. -->
# Regression_albert_3
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7092
- Mse: 0.7092
- Mae: 0.6931
- R2: -0.3058
- Accuracy: 0.4737
## 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: 4
- eval_batch_size: 4
- 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 | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:|
| No log | 1.0 | 33 | 0.3632 | 0.3632 | 0.5672 | -0.0851 | 0.2703 |
| No log | 2.0 | 66 | 0.3855 | 0.3855 | 0.5860 | -0.1518 | 0.2703 |
| No log | 3.0 | 99 | 0.4619 | 0.4619 | 0.5229 | -0.3801 | 0.5405 |
| No log | 4.0 | 132 | 0.4573 | 0.4573 | 0.5791 | -0.3665 | 0.4324 |
| No log | 5.0 | 165 | 0.3254 | 0.3254 | 0.4284 | 0.0277 | 0.7297 |
| No log | 6.0 | 198 | 0.3139 | 0.3139 | 0.4078 | 0.0622 | 0.6757 |
| No log | 7.0 | 231 | 0.3489 | 0.3489 | 0.4370 | -0.0424 | 0.5946 |
| No log | 8.0 | 264 | 0.3933 | 0.3933 | 0.4113 | -0.1753 | 0.6757 |
| No log | 9.0 | 297 | 0.3219 | 0.3219 | 0.3611 | 0.0381 | 0.7027 |
| No log | 10.0 | 330 | 0.3228 | 0.3228 | 0.3423 | 0.0356 | 0.7568 |
| No log | 11.0 | 363 | 0.3289 | 0.3289 | 0.3964 | 0.0173 | 0.6757 |
| No log | 12.0 | 396 | 0.3717 | 0.3717 | 0.3917 | -0.1107 | 0.6757 |
| No log | 13.0 | 429 | 0.4160 | 0.4160 | 0.4238 | -0.2430 | 0.6486 |
| No log | 14.0 | 462 | 0.3691 | 0.3691 | 0.3781 | -0.1027 | 0.6486 |
| No log | 15.0 | 495 | 0.4483 | 0.4483 | 0.4233 | -0.3394 | 0.7027 |
| 0.1519 | 16.0 | 528 | 0.4205 | 0.4205 | 0.3878 | -0.2563 | 0.7027 |
| 0.1519 | 17.0 | 561 | 0.3750 | 0.3750 | 0.4112 | -0.1205 | 0.6216 |
| 0.1519 | 18.0 | 594 | 0.3895 | 0.3895 | 0.4010 | -0.1639 | 0.6486 |
| 0.1519 | 19.0 | 627 | 0.3884 | 0.3884 | 0.3933 | -0.1605 | 0.6757 |
| 0.1519 | 20.0 | 660 | 0.3907 | 0.3907 | 0.3871 | -0.1674 | 0.6757 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln125MNeo
|
[
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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| 12 | 2023-02-27T11:20:53Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: Donut2
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. -->
# Donut2
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) 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: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln6
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 9 | 2023-02-27T11:34:45Z |
---
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: -0.83 +/- 0.27
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
...
```
|
Broadus20/DialoGPT-small-joshua
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
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},
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},
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}
}
}
| 12 | 2023-02-27T13:11:53Z |
---
library_name: stable-baselines3
tags:
- RiverraidNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: RiverraidNoFrameskip-v4
type: RiverraidNoFrameskip-v4
metrics:
- type: mean_reward
value: 6583.00 +/- 765.35
name: mean_reward
verified: false
---
# **PPO** Agent playing **RiverraidNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **RiverraidNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env RiverraidNoFrameskip-v4 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ppo --env RiverraidNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env RiverraidNoFrameskip-v4 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ppo --env RiverraidNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env RiverraidNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env RiverraidNoFrameskip-v4 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 10000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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}
}
| 1,860 | 2023-02-27T13:38:47Z |
---
library_name: stable-baselines3
tags:
- Acrobot-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Acrobot-v1
type: Acrobot-v1
metrics:
- type: mean_reward
value: -85.20 +/- 17.89
name: mean_reward
verified: false
---
# **A2C** Agent playing **Acrobot-v1**
This is a trained model of a **A2C** agent playing **Acrobot-v1**
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 a2c --env Acrobot-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env Acrobot-v1 -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 a2c --env Acrobot-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env Acrobot-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env Acrobot-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env Acrobot-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.0),
('n_envs', 16),
('n_timesteps', 500000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
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"max_length": null,
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}
}
}
| 71 | 2023-02-27T13:44:19Z |
---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
metrics:
- type: mean_reward
value: 281.54 +/- 1.24
name: mean_reward
verified: false
---
# **A2C** Agent playing **BipedalWalker-v3**
This is a trained model of a **A2C** agent playing **BipedalWalker-v3**
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 a2c --env BipedalWalker-v3 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env BipedalWalker-v3 -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 a2c --env BipedalWalker-v3 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env BipedalWalker-v3 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env BipedalWalker-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env BipedalWalker-v3 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.0),
('gae_lambda', 0.9),
('gamma', 0.99),
('learning_rate', 'lin_0.00096'),
('max_grad_norm', 0.5),
('n_envs', 16),
('n_steps', 8),
('n_timesteps', 5000000.0),
('normalize', True),
('normalize_advantage', False),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'),
('use_rms_prop', True),
('use_sde', True),
('vf_coef', 0.4),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 21 | null |
---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
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
---
# **A2C** Agent playing **CartPole-v1**
This is a trained model of a **A2C** agent playing **CartPole-v1**
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 a2c --env CartPole-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env CartPole-v1 -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 a2c --env CartPole-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env CartPole-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env CartPole-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env CartPole-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.0),
('n_envs', 8),
('n_timesteps', 500000.0),
('policy', 'MlpPolicy'),
('normalize', False)])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
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"prefix": null
},
"translation_en_to_fr": {
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},
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"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12 | 2023-02-27T13:48:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 11.82 +/- 142.05
name: mean_reward
verified: false
---
# **A2C** Agent playing **LunarLander-v2**
This is a trained model of a **A2C** agent playing **LunarLander-v2**
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 a2c --env LunarLander-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env LunarLander-v2 -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 a2c --env LunarLander-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env LunarLander-v2 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env LunarLander-v2 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env LunarLander-v2 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 1e-05),
('gamma', 0.995),
('learning_rate', 'lin_0.00083'),
('n_envs', 8),
('n_steps', 5),
('n_timesteps', 200000.0),
('policy', 'MlpPolicy'),
('normalize', False)])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 574 | null |
---
library_name: stable-baselines3
tags:
- LunarLanderContinuous-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLanderContinuous-v2
type: LunarLanderContinuous-v2
metrics:
- type: mean_reward
value: -25.50 +/- 105.78
name: mean_reward
verified: false
---
# **A2C** Agent playing **LunarLanderContinuous-v2**
This is a trained model of a **A2C** agent playing **LunarLanderContinuous-v2**
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 a2c --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env LunarLanderContinuous-v2 -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 a2c --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env LunarLanderContinuous-v2 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env LunarLanderContinuous-v2 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env LunarLanderContinuous-v2 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.0),
('gae_lambda', 0.9),
('gamma', 0.99),
('learning_rate', 'lin_7e-4'),
('max_grad_norm', 0.5),
('n_envs', 4),
('n_steps', 8),
('n_timesteps', 5000000.0),
('normalize', True),
('normalize_advantage', False),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'),
('use_rms_prop', True),
('use_sde', True),
('vf_coef', 0.4),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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},
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},
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},
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"max_length": null,
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}
}
}
| 26 | 2023-02-27T13:49:08Z |
A multidomain model for Sanskrit based on XLM-RoBERTa-base. Accepts Devanagari as input.
|
CAMeL-Lab/bert-base-arabic-camelbert-msa
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2,967 | 2023-02-27T13:49:37Z |
---
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pendulum-v1
type: Pendulum-v1
metrics:
- type: mean_reward
value: -228.96 +/- 128.36
name: mean_reward
verified: false
---
# **A2C** Agent playing **Pendulum-v1**
This is a trained model of a **A2C** agent playing **Pendulum-v1**
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 a2c --env Pendulum-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env Pendulum-v1 -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 a2c --env Pendulum-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env Pendulum-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env Pendulum-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.0),
('gae_lambda', 0.9),
('gamma', 0.9),
('learning_rate', 'lin_7e-4'),
('max_grad_norm', 0.5),
('n_envs', 8),
('n_steps', 8),
('n_timesteps', 1000000.0),
('normalize', True),
('normalize_advantage', False),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'),
('use_rms_prop', True),
('use_sde', True),
('vf_coef', 0.4),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
CLTL/MedRoBERTa.nl
|
[
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 2,988 | null |
---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
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
---
# **A2C** Agent playing **CartPole-v1**
This is a trained model of a **A2C** agent playing **CartPole-v1**
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 a2c --env CartPole-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env CartPole-v1 -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 a2c --env CartPole-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env CartPole-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env CartPole-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env CartPole-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.0),
('n_envs', 8),
('n_timesteps', 500000.0),
('policy', 'MlpPolicy'),
('normalize', False)])
```
|
Cameron/BERT-jigsaw-severetoxic
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 30 | null |
---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ARS
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
---
# **ARS** Agent playing **CartPole-v1**
This is a trained model of a **ARS** agent playing **CartPole-v1**
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 ars --env CartPole-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ars --env CartPole-v1 -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 ars --env CartPole-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ars --env CartPole-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ars --env CartPole-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ars --env CartPole-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('n_delta', 2),
('n_envs', 1),
('n_timesteps', 50000.0),
('policy', 'LinearPolicy'),
('normalize', False)])
```
|
Captain-1337/CrudeBERT
|
[
"pytorch",
"bert",
"text-classification",
"arxiv:1908.10063",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 28 | null |
---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
metrics:
- type: mean_reward
value: 3033.34 +/- 20.17
name: mean_reward
verified: false
---
# **SAC** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **SAC** agent playing **HalfCheetahBulletEnv-v0**
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 sac --env HalfCheetahBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env HalfCheetahBulletEnv-v0 -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 sac --env HalfCheetahBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env HalfCheetahBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env HalfCheetahBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env HalfCheetahBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 300000),
('ent_coef', 'auto'),
('gamma', 0.98),
('gradient_steps', 8),
('learning_rate', 0.00073),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-3, net_arch=[400, 300])'),
('tau', 0.02),
('train_freq', 8),
('use_sde', True),
('normalize', False)])
```
|
Captain272/lstm
|
[] | null |
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| 0 | null |
---
library_name: stable-baselines3
tags:
- MinitaurBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MinitaurBulletEnv-v0
type: MinitaurBulletEnv-v0
metrics:
- type: mean_reward
value: 5.55 +/- 6.24
name: mean_reward
verified: false
---
# **SAC** Agent playing **MinitaurBulletEnv-v0**
This is a trained model of a **SAC** agent playing **MinitaurBulletEnv-v0**
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 sac --env MinitaurBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env MinitaurBulletEnv-v0 -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 sac --env MinitaurBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env MinitaurBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env MinitaurBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env MinitaurBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 100000),
('ent_coef', 'auto'),
('gradient_steps', 1),
('learning_rate', 0.0003),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('train_freq', 1),
('normalize', False)])
```
|
dccuchile/albert-large-spanish-finetuned-mldoc
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
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"AlbertForSequenceClassification"
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}
}
| 27 | null |
---
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pendulum-v1
type: Pendulum-v1
metrics:
- type: mean_reward
value: -184.35 +/- 106.90
name: mean_reward
verified: false
---
# **SAC** Agent playing **Pendulum-v1**
This is a trained model of a **SAC** agent playing **Pendulum-v1**
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 sac --env Pendulum-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env Pendulum-v1 -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 sac --env Pendulum-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo sac --env Pendulum-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo sac --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo sac --env Pendulum-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('learning_rate', 0.001),
('n_timesteps', 20000),
('policy', 'MlpPolicy'),
('normalize', False)])
```
|
dccuchile/albert-tiny-spanish-finetuned-xnli
|
[
"pytorch",
"albert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"AlbertForSequenceClassification"
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}
| 31 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- spider
model-index:
- name: t5-small-finetuned-spider-wo_db
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-spider-wo_db
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the spider dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7198
- Rouge2 Precision: 0.418
- Rouge2 Recall: 0.2665
- Rouge2 Fmeasure: 0.3074
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log | 1.0 | 438 | 0.8903 | 0.3327 | 0.2179 | 0.2435 |
| 1.5339 | 2.0 | 876 | 0.7760 | 0.3853 | 0.2499 | 0.2831 |
| 0.825 | 3.0 | 1314 | 0.7418 | 0.403 | 0.2594 | 0.2963 |
| 0.7137 | 4.0 | 1752 | 0.7250 | 0.4092 | 0.2612 | 0.3011 |
| 0.6653 | 5.0 | 2190 | 0.7198 | 0.418 | 0.2665 | 0.3074 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
dccuchile/distilbert-base-spanish-uncased-finetuned-ner
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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}
}
| 28 | 2023-02-27T15:24:50Z |
---
library_name: stable-baselines3
tags:
- MountainCarContinuous-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCarContinuous-v0
type: MountainCarContinuous-v0
metrics:
- type: mean_reward
value: 93.36 +/- 0.11
name: mean_reward
verified: false
---
# **TD3** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **TD3** agent playing **MountainCarContinuous-v0**
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 td3 --env MountainCarContinuous-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env MountainCarContinuous-v0 -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 td3 --env MountainCarContinuous-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env MountainCarContinuous-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo td3 --env MountainCarContinuous-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo td3 --env MountainCarContinuous-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('n_timesteps', 300000),
('noise_std', 0.5),
('noise_type', 'ornstein-uhlenbeck'),
('policy', 'MlpPolicy'),
('normalize', False)])
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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}
}
| 29 | null |
---
library_name: stable-baselines3
tags:
- InvertedPendulumSwingupBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: InvertedPendulumSwingupBulletEnv-v0
type: InvertedPendulumSwingupBulletEnv-v0
metrics:
- type: mean_reward
value: 888.56 +/- 0.89
name: mean_reward
verified: false
---
# **TD3** Agent playing **InvertedPendulumSwingupBulletEnv-v0**
This is a trained model of a **TD3** agent playing **InvertedPendulumSwingupBulletEnv-v0**
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 td3 --env InvertedPendulumSwingupBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env InvertedPendulumSwingupBulletEnv-v0 -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 td3 --env InvertedPendulumSwingupBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env InvertedPendulumSwingupBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo td3 --env InvertedPendulumSwingupBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo td3 --env InvertedPendulumSwingupBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', -1),
('learning_rate', 0.001),
('learning_starts', 10000),
('n_timesteps', 300000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', [1, 'episode']),
('normalize', False)])
```
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-1
|
[
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
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},
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"max_length": null,
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}
}
}
| 7 | 2023-02-27T15:28:23Z |
---
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pendulum-v1
type: Pendulum-v1
metrics:
- type: mean_reward
value: -178.65 +/- 102.84
name: mean_reward
verified: false
---
# **TD3** Agent playing **Pendulum-v1**
This is a trained model of a **TD3** agent playing **Pendulum-v1**
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 td3 --env Pendulum-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env Pendulum-v1 -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 td3 --env Pendulum-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env Pendulum-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo td3 --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo td3 --env Pendulum-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', -1),
('learning_rate', 0.001),
('learning_starts', 10000),
('n_timesteps', 20000),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', [1, 'episode']),
('normalize', False)])
```
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1
|
[
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
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}
}
}
| 1 | 2023-02-27T15:29:00Z |
---
library_name: stable-baselines3
tags:
- LunarLanderContinuous-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLanderContinuous-v2
type: LunarLanderContinuous-v2
metrics:
- type: mean_reward
value: 207.84 +/- 84.51
name: mean_reward
verified: false
---
# **TD3** Agent playing **LunarLanderContinuous-v2**
This is a trained model of a **TD3** agent playing **LunarLanderContinuous-v2**
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 td3 --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env LunarLanderContinuous-v2 -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 td3 --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env LunarLanderContinuous-v2 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo td3 --env LunarLanderContinuous-v2 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo td3 --env LunarLanderContinuous-v2 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', -1),
('learning_rate', 0.001),
('learning_starts', 10000),
('n_timesteps', 300000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', [1, 'episode']),
('normalize', False)])
```
|
Chaima/TunBerto
|
[] | null |
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}
| 0 | 2023-02-27T15:34:11Z |
---
library_name: stable-baselines3
tags:
- parking-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: parking-v0
type: parking-v0
metrics:
- type: mean_reward
value: -8.78 +/- 3.50
name: mean_reward
verified: false
---
# **TQC** Agent playing **parking-v0**
This is a trained model of a **TQC** agent playing **parking-v0**
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 tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -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 tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo tqc --env parking-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env parking-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('buffer_size', 1000000),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gamma', 0.98),
('learning_rate', 0.0015),
('n_timesteps', 50000.0),
('policy', 'MultiInputPolicy'),
('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
('replay_buffer_class', 'HerReplayBuffer'),
('replay_buffer_kwargs',
"dict( online_sampling=False, goal_selection_strategy='episode', "
'n_sampled_goal=4, max_episode_length=100 )'),
('tau', 0.005),
('normalize', False)])
```
|
Chandanbhat/distilbert-base-uncased-finetuned-cola
|
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}
| 0 | null |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: base-3-3
results:
- task:
name: Summarization
type: summarization
dataset:
name: xsum
type: xsum
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 34.9185
---
<!-- 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-3-3
This model is a fine-tuned version of [x/base-3-3/](https://huggingface.co/x/base-3-3/) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0374
- Rouge1: 34.9185
- Rouge2: 12.4425
- Rougel: 27.5675
- Rougelsum: 27.5602
- Gen Len: 27.7195
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.10.0
- Tokenizers 0.13.2
|
ChristopherA08/IndoELECTRA
|
[
"pytorch",
"electra",
"pretraining",
"id",
"dataset:oscar",
"transformers"
] | null |
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| 4 | null |
---
library_name: stable-baselines3
tags:
- PandaPush-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPush-v1
type: PandaPush-v1
metrics:
- type: mean_reward
value: -7.90 +/- 3.11
name: mean_reward
verified: false
---
# **TQC** Agent playing **PandaPush-v1**
This is a trained model of a **TQC** agent playing **PandaPush-v1**
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 tqc --env PandaPush-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env PandaPush-v1 -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 tqc --env PandaPush-v1 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env PandaPush-v1 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo tqc --env PandaPush-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env PandaPush-v1 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 2048),
('buffer_size', 1000000),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gamma', 0.95),
('learning_rate', 0.001),
('n_timesteps', 1000000.0),
('policy', 'MultiInputPolicy'),
('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
('replay_buffer_class', 'HerReplayBuffer'),
('replay_buffer_kwargs',
"dict( online_sampling=True, goal_selection_strategy='future', "
'n_sampled_goal=4, )'),
('tau', 0.05),
('normalize', False)])
```
# Environment Arguments
```python
{'render': True}
```
|
ChukSamuels/DialoGPT-small-Dr.FauciBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 13 | null |
---
library_name: stable-baselines3
tags:
- parking-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: parking-v0
type: parking-v0
metrics:
- type: mean_reward
value: -7.43 +/- 2.87
name: mean_reward
verified: false
---
# **TQC** Agent playing **parking-v0**
This is a trained model of a **TQC** agent playing **parking-v0**
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 tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -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 tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo tqc --env parking-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env parking-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('buffer_size', 1000000),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gamma', 0.98),
('learning_rate', 0.0015),
('n_timesteps', 50000.0),
('policy', 'MultiInputPolicy'),
('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
('replay_buffer_class', 'HerReplayBuffer'),
('replay_buffer_kwargs',
"dict( online_sampling=False, goal_selection_strategy='episode', "
'n_sampled_goal=4, max_episode_length=100 )'),
('tau', 0.005),
('normalize', False)])
```
|
Chun/DialoGPT-large-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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}
| 6 | null |
---
library_name: stable-baselines3
tags:
- parking-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TQC
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: parking-v0
type: parking-v0
metrics:
- type: mean_reward
value: -7.07 +/- 2.36
name: mean_reward
verified: false
---
# **TQC** Agent playing **parking-v0**
This is a trained model of a **TQC** agent playing **parking-v0**
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 tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -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 tqc --env parking-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo tqc --env parking-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo tqc --env parking-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo tqc --env parking-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('buffer_size', 1000000),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gamma', 0.98),
('learning_rate', 0.0015),
('n_timesteps', 50000.0),
('policy', 'MultiInputPolicy'),
('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
('replay_buffer_class', 'HerReplayBuffer'),
('replay_buffer_kwargs',
"dict( online_sampling=False, goal_selection_strategy='episode', "
'n_sampled_goal=4, max_episode_length=100 )'),
('tau', 0.005),
('normalize', False)])
```
|
Chungu424/qazwsx
|
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}
| 0 | null |
---
library_name: stable-baselines3
tags:
- LunarLanderContinuous-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TRPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLanderContinuous-v2
type: LunarLanderContinuous-v2
metrics:
- type: mean_reward
value: 233.11 +/- 15.60
name: mean_reward
verified: false
---
# **TRPO** Agent playing **LunarLanderContinuous-v2**
This is a trained model of a **TRPO** agent playing **LunarLanderContinuous-v2**
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 trpo --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo trpo --env LunarLanderContinuous-v2 -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 trpo --env LunarLanderContinuous-v2 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo trpo --env LunarLanderContinuous-v2 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo trpo --env LunarLanderContinuous-v2 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo trpo --env LunarLanderContinuous-v2 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('n_critic_updates', 20),
('n_envs', 2),
('n_steps', 1024),
('n_timesteps', 100000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
Cilan/dalle-knockoff
|
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}
| 0 | 2023-02-27T16:19:31Z |
---
library_name: stable-baselines3
tags:
- HalfCheetahBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetahBulletEnv-v0
type: HalfCheetahBulletEnv-v0
metrics:
- type: mean_reward
value: 2562.76 +/- 118.13
name: mean_reward
verified: false
---
# **DDPG** Agent playing **HalfCheetahBulletEnv-v0**
This is a trained model of a **DDPG** agent playing **HalfCheetahBulletEnv-v0**
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 ddpg --env HalfCheetahBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env HalfCheetahBulletEnv-v0 -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 ddpg --env HalfCheetahBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env HalfCheetahBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ddpg --env HalfCheetahBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ddpg --env HalfCheetahBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', 1),
('learning_rate', 0.001),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', 1),
('normalize', False)])
```
|
Cinnamon/electra-small-japanese-discriminator
|
[
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null |
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"ElectraForPreTraining"
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}
| 419 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_new_split_0060
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. -->
# whisper_new_split_0060
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0017
- Train Accuracy: 0.0335
- Train Wermet: 34.4652
- Validation Loss: 0.5023
- Validation Accuracy: 0.0316
- Validation Wermet: 33.7523
- Epoch: 59
## 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': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 5.1027 | 0.0113 | 52.5530 | 4.4267 | 0.0121 | 41.4796 | 0 |
| 4.3285 | 0.0126 | 38.6893 | 3.9835 | 0.0145 | 33.6050 | 1 |
| 3.4573 | 0.0168 | 30.7714 | 2.5568 | 0.0215 | 31.7559 | 2 |
| 2.0878 | 0.0226 | 20.5131 | 1.5738 | 0.0257 | 21.2159 | 3 |
| 1.3529 | 0.0258 | 17.4367 | 1.1712 | 0.0276 | 17.7695 | 4 |
| 0.9953 | 0.0275 | 18.7308 | 0.9389 | 0.0287 | 20.5259 | 5 |
| 0.7852 | 0.0286 | 18.5731 | 0.8074 | 0.0294 | 17.6576 | 6 |
| 0.6428 | 0.0293 | 18.2945 | 0.7219 | 0.0298 | 19.9850 | 7 |
| 0.5384 | 0.0299 | 18.9258 | 0.6610 | 0.0301 | 18.9327 | 8 |
| 0.4565 | 0.0304 | 19.0749 | 0.6117 | 0.0304 | 21.9796 | 9 |
| 0.3901 | 0.0308 | 19.2099 | 0.5693 | 0.0306 | 18.0965 | 10 |
| 0.3348 | 0.0312 | 20.4777 | 0.5449 | 0.0307 | 19.9518 | 11 |
| 0.2877 | 0.0315 | 20.3181 | 0.5232 | 0.0309 | 20.4017 | 12 |
| 0.2471 | 0.0318 | 19.2073 | 0.5057 | 0.0310 | 18.7612 | 13 |
| 0.2120 | 0.0320 | 19.0961 | 0.4925 | 0.0311 | 22.3187 | 14 |
| 0.1809 | 0.0323 | 20.7944 | 0.4849 | 0.0311 | 27.2314 | 15 |
| 0.1539 | 0.0325 | 22.0951 | 0.4787 | 0.0312 | 25.2171 | 16 |
| 0.1299 | 0.0327 | 22.7652 | 0.4733 | 0.0312 | 22.7492 | 17 |
| 0.1087 | 0.0329 | 25.2223 | 0.4701 | 0.0312 | 28.9044 | 18 |
| 0.0899 | 0.0330 | 24.8354 | 0.4715 | 0.0313 | 21.1618 | 19 |
| 0.0739 | 0.0332 | 25.4987 | 0.4680 | 0.0313 | 29.6304 | 20 |
| 0.0604 | 0.0333 | 27.6465 | 0.4693 | 0.0313 | 27.6937 | 21 |
| 0.0498 | 0.0333 | 27.7045 | 0.4711 | 0.0313 | 27.5013 | 22 |
| 0.0414 | 0.0334 | 28.0547 | 0.4689 | 0.0313 | 29.1776 | 23 |
| 0.0327 | 0.0334 | 27.5594 | 0.4718 | 0.0313 | 31.5623 | 24 |
| 0.0256 | 0.0335 | 27.3983 | 0.4710 | 0.0313 | 27.1071 | 25 |
| 0.0210 | 0.0335 | 24.7398 | 0.4736 | 0.0313 | 30.8282 | 26 |
| 0.0165 | 0.0335 | 25.1927 | 0.4773 | 0.0313 | 24.1750 | 27 |
| 0.0133 | 0.0335 | 25.6261 | 0.4807 | 0.0313 | 29.9520 | 28 |
| 0.0110 | 0.0335 | 25.8127 | 0.4825 | 0.0314 | 27.0813 | 29 |
| 0.0171 | 0.0335 | 26.0445 | 0.4858 | 0.0313 | 39.8503 | 30 |
| 0.0154 | 0.0335 | 28.6186 | 0.4766 | 0.0314 | 28.4465 | 31 |
| 0.0094 | 0.0335 | 27.8978 | 0.4778 | 0.0314 | 28.7775 | 32 |
| 0.0071 | 0.0335 | 27.8180 | 0.4775 | 0.0314 | 28.5229 | 33 |
| 0.0054 | 0.0335 | 27.4530 | 0.4807 | 0.0315 | 30.3598 | 34 |
| 0.0043 | 0.0335 | 27.2908 | 0.4833 | 0.0315 | 29.9185 | 35 |
| 0.0036 | 0.0335 | 27.9772 | 0.4870 | 0.0315 | 29.0761 | 36 |
| 0.0031 | 0.0335 | 29.0235 | 0.4901 | 0.0315 | 31.1068 | 37 |
| 0.0027 | 0.0335 | 28.2433 | 0.4930 | 0.0315 | 30.2512 | 38 |
| 0.0024 | 0.0335 | 33.0830 | 0.4968 | 0.0315 | 35.0547 | 39 |
| 0.0152 | 0.0334 | 30.0515 | 0.4999 | 0.0314 | 31.3169 | 40 |
| 0.0095 | 0.0335 | 30.0595 | 0.4917 | 0.0315 | 25.2631 | 41 |
| 0.0038 | 0.0335 | 23.3205 | 0.4882 | 0.0315 | 22.6513 | 42 |
| 0.0028 | 0.0335 | 23.9223 | 0.4847 | 0.0315 | 28.0730 | 43 |
| 0.0023 | 0.0335 | 28.1808 | 0.4919 | 0.0315 | 33.1382 | 44 |
| 0.0036 | 0.0335 | 32.2064 | 0.4898 | 0.0315 | 27.6573 | 45 |
| 0.0032 | 0.0335 | 31.5153 | 0.4964 | 0.0315 | 38.2573 | 46 |
| 0.0025 | 0.0335 | 35.6323 | 0.4925 | 0.0315 | 24.0359 | 47 |
| 0.0023 | 0.0335 | 30.3651 | 0.4937 | 0.0315 | 29.2069 | 48 |
| 0.0023 | 0.0335 | 32.4053 | 0.4968 | 0.0315 | 38.3220 | 49 |
| 0.0020 | 0.0335 | 39.4820 | 0.5002 | 0.0315 | 35.0649 | 50 |
| 0.0021 | 0.0335 | 33.6737 | 0.5188 | 0.0314 | 32.5078 | 51 |
| 0.0057 | 0.0335 | 31.5887 | 0.5069 | 0.0315 | 29.0739 | 52 |
| 0.0053 | 0.0335 | 30.3396 | 0.4958 | 0.0316 | 30.4228 | 53 |
| 0.0035 | 0.0335 | 30.4281 | 0.4911 | 0.0316 | 30.4131 | 54 |
| 0.0019 | 0.0335 | 36.0578 | 0.4909 | 0.0316 | 39.2006 | 55 |
| 0.0013 | 0.0335 | 36.1010 | 0.4927 | 0.0316 | 33.9386 | 56 |
| 0.0011 | 0.0335 | 30.4648 | 0.4979 | 0.0316 | 35.2314 | 57 |
| 0.0023 | 0.0335 | 37.5041 | 0.4983 | 0.0316 | 34.6155 | 58 |
| 0.0017 | 0.0335 | 34.4652 | 0.5023 | 0.0316 | 33.7523 | 59 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.11.0
- Tokenizers 0.13.2
|
Cinnamon/electra-small-japanese-generator
|
[
"pytorch",
"electra",
"fill-mask",
"ja",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"ElectraForMaskedLM"
],
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}
| 19 | 2023-02-27T16:20:29Z |
---
library_name: stable-baselines3
tags:
- HumanoidBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HumanoidBulletEnv-v0
type: HumanoidBulletEnv-v0
metrics:
- type: mean_reward
value: -39.87 +/- 8.87
name: mean_reward
verified: false
---
# **DDPG** Agent playing **HumanoidBulletEnv-v0**
This is a trained model of a **DDPG** agent playing **HumanoidBulletEnv-v0**
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 ddpg --env HumanoidBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env HumanoidBulletEnv-v0 -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 ddpg --env HumanoidBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env HumanoidBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ddpg --env HumanoidBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ddpg --env HumanoidBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', -1),
('learning_rate', 0.001),
('learning_starts', 10000),
('n_timesteps', 2000000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', [1, 'episode']),
('normalize', False)])
```
|
Ciruzzo/DialoGPT-medium-harrypotter
|
[] | null |
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}
}
| 0 | 2023-02-27T16:21:10Z |
---
license: creativeml-openrail-m
---
---
CHECKPOINT MERGE of STABLE DIFFUSION :
---
- </b>merging from other checkpoint</b>
---
https://huggingface.co/nitrosocke/Nitro-Diffusion
---
https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0
---
https://huggingface.co/Froddan/postapocalypse
---
https://civitai.com/models/3816/protogen-x53-photorealism-official-release
---
https://civitai.com/models/3811/dreamlike-photoreal-20
---
|
Ciruzzo/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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}
}
}
| 9 | 2023-02-27T16:21:15Z |
---
library_name: stable-baselines3
tags:
- MountainCarContinuous-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCarContinuous-v0
type: MountainCarContinuous-v0
metrics:
- type: mean_reward
value: 93.72 +/- 0.13
name: mean_reward
verified: false
---
# **DDPG** Agent playing **MountainCarContinuous-v0**
This is a trained model of a **DDPG** agent playing **MountainCarContinuous-v0**
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 ddpg --env MountainCarContinuous-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env MountainCarContinuous-v0 -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 ddpg --env MountainCarContinuous-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env MountainCarContinuous-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ddpg --env MountainCarContinuous-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ddpg --env MountainCarContinuous-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('n_timesteps', 300000),
('noise_std', 0.5),
('noise_type', 'ornstein-uhlenbeck'),
('policy', 'MlpPolicy'),
('normalize', False)])
```
|
Ciruzzo/DialoGPT-small-hattypotter
|
[] | null |
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}
| 0 | null |
---
library_name: stable-baselines3
tags:
- InvertedPendulumSwingupBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: InvertedPendulumSwingupBulletEnv-v0
type: InvertedPendulumSwingupBulletEnv-v0
metrics:
- type: mean_reward
value: 889.41 +/- 1.27
name: mean_reward
verified: false
---
# **DDPG** Agent playing **InvertedPendulumSwingupBulletEnv-v0**
This is a trained model of a **DDPG** agent playing **InvertedPendulumSwingupBulletEnv-v0**
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 ddpg --env InvertedPendulumSwingupBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env InvertedPendulumSwingupBulletEnv-v0 -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 ddpg --env InvertedPendulumSwingupBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env InvertedPendulumSwingupBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ddpg --env InvertedPendulumSwingupBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ddpg --env InvertedPendulumSwingupBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', 1),
('learning_rate', 0.001),
('learning_starts', 10000),
('n_timesteps', 300000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', 1),
('normalize', False)])
```
|
ClaudeCOULOMBE/RickBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
}
| 9 | 2023-02-27T16:22:24Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DDPG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 3198.32 +/- 973.60
name: mean_reward
verified: false
---
# **DDPG** Agent playing **AntBulletEnv-v0**
This is a trained model of a **DDPG** agent playing **AntBulletEnv-v0**
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 ddpg --env AntBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env AntBulletEnv-v0 -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 ddpg --env AntBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo ddpg --env AntBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ddpg --env AntBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ddpg --env AntBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', 1),
('learning_rate', 0.0007),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', 1),
('normalize', False)])
```
|
CleveGreen/FieldClassifier
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
| 34 | null |
Access to model Faisalrahi/hate_speech is restricted and you are not in the authorized list. Visit https://huggingface.co/Faisalrahi/hate_speech to ask for access.
|
CodeNinja1126/xlm-roberta-large-kor-mrc
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 8 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-finetuned-ner-per-v9
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. -->
# bert-finetuned-ner-per-v9
This model is a fine-tuned version of [BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2](https://huggingface.co/BeardedJohn/bert-finetuned-ner-ubb-conll-endava-only-misc-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 128, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2
|
CoderBoy432/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 11 | null |
---
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
|
Cometasonmi451/Mine
|
[] | null |
{
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}
}
}
| 0 | null |
---
language:
- pt
library_name: pysentimiento
tags:
- twitter
- sentiment-analysis
---
# Sentiment Analysis in Portuguese
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/pysentimiento/pysentimiento/)
Model trained for polarity detection in Portuguese. Base model is [BERTabaporu](https://huggingface.co/pablocosta/bertabaporu-base-uncased), a RoBERTa model trained in Portuguese tweets.
Uses `POS`, `NEG`, `NEU` labels.
## Usage
Use it directly with [pysentimiento](https://github.com/pysentimiento/pysentimiento)
```python
from pysentimiento import create_analyzer
analyzer = create_analyzer(task="sentiment", lang="pt")
analyzer.predict("isto é bonito")
# returns AnalyzerOutput(output=POS, probas={POS: 0.998, NEG: 0.002, NEU: 0.000})
```
## Citation
If you use this model in your research, please cite pysentimiento and RoBERTuito papers:
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc {pablo_botton_da_costa_2022,
author = { {pablo botton da costa} },
title = { bertabaporu-base-uncased (Revision 1982d0f) },
year = 2022,
url = { https://huggingface.co/pablocosta/bertabaporu-base-uncased },
doi = { 10.57967/hf/0019 },
publisher = { Hugging Face }
}
@InProceedings{BRUM18.389,
author = {Henrico Brum and Maria das Gra\c{c}as Volpe Nunes},
title = "{Building a Sentiment Corpus of Tweets in Brazilian Portuguese}",
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {May 7-12, 2018},
address = {Miyazaki, Japan},
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and HÚlŔne Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
publisher = {European Language Resources Association (ELRA)},
isbn = {979-10-95546-00-9},
language = {english}
}
```
|
Connor/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"early_stopping": null,
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}
}
| 7 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Indigot Dreambooth model trained by kasgoss with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
.png)
.png)
|
Connor-tech/bert_cn_finetuning
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"min_length": null,
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},
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},
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},
"translation_en_to_fr": {
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}
}
}
| 27 | null |
---
language:
- pt
library_name: pysentimiento
tags:
- twitter
- hate-speech
---
# Hate speech detection in Portuguese
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/pysentimiento/pysentimiento/)
Model trained for hate speech detection in Portuguese. Base model is [BERTabaporu](https://huggingface.co/pablocosta/bertabaporu-base-uncased), a RoBERTa model trained in Portuguese tweets.
## Usage
Use it directly with [pysentimiento](https://github.com/pysentimiento/pysentimiento)
```python
from pysentimiento import create_analyzer
analyzer = create_analyzer(task="hate_speech", lang="pt")
analyzer.predict("você tem que matar todos os malditos negros")
# Returns AnalyzerOutput(output=['Racism'], probas={Sexism: 0.027, Body: 0.016, Racism: 0.698, Ideology: 0.025, Homophobia: 0.017})
```
## Citation
If you use this model in your research, please cite pysentimiento and RoBERTuito papers:
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc {pablo_botton_da_costa_2022,
author = { {pablo botton da costa} },
title = { bertabaporu-base-uncased (Revision 1982d0f) },
year = 2022,
url = { https://huggingface.co/pablocosta/bertabaporu-base-uncased },
doi = { 10.57967/hf/0019 },
publisher = { Hugging Face }
}
@inproceedings{fortuna-etal-2019-hierarchically,
title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset",
author = "Fortuna, Paula and
Rocha da Silva, Jo{\~a}o and
Soler-Company, Juan and
Wanner, Leo and
Nunes, S{\'e}rgio",
booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3510",
doi = "10.18653/v1/W19-3510",
pages = "94--104",
abstract = "Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning are applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels ({`}hate{'} vs. {`}no-hate{'}). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.",
}
```
|
Contrastive-Tension/BERT-Base-NLI-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 9 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -140.98 +/- 39.80
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': 'sheryliza/lunarlander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Contrastive-Tension/BERT-Distil-CT-STSb
|
[
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
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}
}
| 1 | null |
---
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
|
Contrastive-Tension/BERT-Distil-CT
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"DistilBertForMaskedLM"
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"model_type": "distilbert",
"task_specific_params": {
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}
| 9 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-finetuned-v4-seinfeld
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-finetuned-v4-seinfeld
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6941
## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1734 | 0.79 | 8 | 2.9417 |
| 3.1576 | 1.59 | 16 | 2.9239 |
| 3.1234 | 2.4 | 24 | 2.8920 |
| 3.0712 | 3.2 | 32 | 2.8634 |
| 2.9738 | 3.99 | 40 | 2.8342 |
| 2.9761 | 4.79 | 48 | 2.8069 |
| 2.9294 | 5.59 | 56 | 2.7844 |
| 2.9026 | 6.4 | 64 | 2.7665 |
| 2.8501 | 7.2 | 72 | 2.7544 |
| 2.7805 | 7.99 | 80 | 2.7398 |
| 2.7905 | 8.79 | 88 | 2.7293 |
| 2.7661 | 9.59 | 96 | 2.7204 |
| 2.7272 | 10.4 | 104 | 2.7131 |
| 2.7092 | 11.2 | 112 | 2.7056 |
| 2.6392 | 11.99 | 120 | 2.7010 |
| 2.6468 | 12.79 | 128 | 2.6961 |
| 2.6269 | 13.59 | 136 | 2.6899 |
| 2.5952 | 14.4 | 144 | 2.6874 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Corvus/DialoGPT-medium-CaptainPrice
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
| 7 | null |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: finetuned-c2c-base-v2-french-financial-summarization
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. -->
# finetuned-c2c-base-v2-french-financial-summarization
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8416
- Rouge1: 11.4192
- Rouge2: 4.3593
- Rougel: 9.4634
- Rougelsum: 10.1881
- Gen Len: 20.0
- Bertscore: 0.6317
- Bartscore: 0.3216
- Bleurt: -1.1537
- Meteor: 0.0474
- Frugal Score (mover-score): 0.0645
- Frugal Score (bert-score): 0.0645
- Cider: 0.0000
- Infolm Kl Divergence: -2.1462
- Infolm Beta Divergence: 1.545
- Infolm L1 Distance: 1.2895
- Infolm Fisher Rao Distance: 1.7961
- Baryscore: 0.8909
- Depthscore: 0.1517
## 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: 3
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bertscore | Bartscore | Bleurt | Meteor | Frugal Score (mover-score) | Frugal Score (bert-score) | Cider | Infolm Kl Divergence | Infolm Beta Divergence | Infolm L1 Distance | Infolm Fisher Rao Distance | Baryscore | Depthscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|:---------:|:---------:|:-------:|:------:|:--------------------------:|:-------------------------:|:------:|:--------------------:|:----------------------:|:------------------:|:--------------------------:|:---------:|:----------:|
| 5.7736 | 1.0 | 388 | 3.6079 | 11.8815 | 3.015 | 8.7586 | 9.5237 | 20.0 | 0.6353 | 0.3216 | -1.1949 | 0.0531 | 0.1039 | 0.1039 | 0.0001 | -1.956 | 1.4825 | 1.2665 | 1.7855 | 0.8321 | 0.1639 |
| 3.3952 | 2.0 | 776 | 3.0201 | 10.9955 | 2.2526 | 8.1102 | 9.6629 | 20.0 | 0.6300 | 0.3399 | -1.1080 | 0.0515 | 0.0735 | 0.0735 | 0.0000 | -1.9037 | 1.4307 | 1.2641 | 1.7455 | 0.8327 | 0.1332 |
| 2.8124 | 3.0 | 1164 | 2.8314 | 10.4805 | 3.5522 | 8.941 | 9.512 | 20.0 | 0.6338 | 0.3324 | -1.2350 | 0.049 | 0.0720 | 0.0720 | 0.0000 | -1.8864 | 1.3916 | 1.2283 | 1.705 | 0.9155 | 0.1441 |
| 2.4492 | 4.0 | 1552 | 2.7542 | 10.3645 | 2.9289 | 8.1838 | 9.124 | 20.0 | 0.6150 | 0.3125 | -1.1116 | 0.0416 | 0.0781 | 0.0781 | 0.0002 | -2.1287 | 1.5708 | 1.3025 | 1.7939 | 0.9089 | 0.1474 |
| 2.1894 | 5.0 | 1940 | 2.7051 | 11.3038 | 3.4858 | 9.243 | 9.8498 | 20.0 | 0.6305 | 0.3208 | -1.1235 | 0.0501 | 0.0903 | 0.0903 | 0.0000 | -2.0454 | 1.4924 | 1.2821 | 1.7942 | 0.8983 | 0.1517 |
| 1.9737 | 6.0 | 2328 | 2.7271 | 11.3296 | 3.5841 | 9.1366 | 9.9143 | 20.0 | 0.6255 | 0.3210 | -1.1878 | 0.0458 | 0.0700 | 0.0700 | 0.0000 | -2.1004 | 1.58 | 1.3076 | 1.8118 | 0.8974 | 0.1513 |
| 1.7791 | 7.0 | 2716 | 2.7617 | 11.4249 | 3.6825 | 9.4058 | 10.177 | 20.0 | 0.6309 | 0.3304 | -1.1455 | 0.0497 | 0.0639 | 0.0639 | 0.0000 | -2.1191 | 1.5301 | 1.2868 | 1.7942 | 0.8957 | 0.1464 |
| 1.6378 | 8.0 | 3104 | 2.7806 | 11.4122 | 3.7302 | 9.2744 | 9.9941 | 20.0 | 0.6292 | 0.3251 | -1.1447 | 0.0491 | 0.0737 | 0.0737 | 0.0000 | -2.0768 | 1.52 | 1.2793 | 1.7807 | 0.8373 | 0.1517 |
| 1.523 | 9.0 | 3492 | 2.8181 | 11.3777 | 4.0711 | 9.3188 | 9.9887 | 20.0 | 0.6306 | 0.3198 | -1.1544 | 0.0461 | 0.0629 | 0.0629 | 0.0000 | -2.2015 | 1.6039 | 1.3047 | 1.8182 | 0.887 | 0.1515 |
| 1.4398 | 10.0 | 3880 | 2.8416 | 11.4192 | 4.3593 | 9.4634 | 10.1881 | 20.0 | 0.6317 | 0.3216 | -1.1537 | 0.0474 | 0.0645 | 0.0645 | 0.0000 | -2.1462 | 1.545 | 1.2895 | 1.7961 | 0.8909 | 0.1517 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
|
CouchCat/ma_ner_v6_distil
|
[
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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}
| 6 | null |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: opt-350m-finetuned-v4-seinfeld
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. -->
# opt-350m-finetuned-v4-seinfeld
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4775
## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8604 | 0.79 | 8 | 2.7539 |
| 2.8058 | 1.59 | 16 | 2.6860 |
| 2.7427 | 2.4 | 24 | 2.6338 |
| 2.6593 | 3.2 | 32 | 2.5925 |
| 2.5373 | 3.99 | 40 | 2.5601 |
| 2.4923 | 4.79 | 48 | 2.5370 |
| 2.4102 | 5.59 | 56 | 2.5216 |
| 2.3373 | 6.4 | 64 | 2.5049 |
| 2.2341 | 7.2 | 72 | 2.4963 |
| 2.1286 | 7.99 | 80 | 2.4862 |
| 2.0673 | 8.79 | 88 | 2.4908 |
| 1.9938 | 9.59 | 96 | 2.4881 |
| 1.9015 | 10.4 | 104 | 2.4854 |
| 1.8172 | 11.2 | 112 | 2.5058 |
| 1.7113 | 11.99 | 120 | 2.4950 |
| 1.6409 | 12.79 | 128 | 2.5082 |
| 1.5622 | 13.59 | 136 | 2.5172 |
| 1.4724 | 14.4 | 144 | 2.5464 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
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