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
|
|---|---|---|---|---|---|---|
Bia18/Beatriz | []
| null | {
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} | 0 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 52.40 +/- 38.12
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Biasface/DDDC2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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}
} | 10 | null | ---
license: bsd-3-clause
---
# H3 Language Model
Official model weights for [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://arxiv.org/abs/2212.14052).
See our [GitHub](https://github.com/HazyResearch/H3) for instructions on how to download and run the model! |
BigBoy/model | []
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} | 0 | null | ---
license: bsd-3-clause
---
# H3 Language Model
Official model weights for [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://arxiv.org/abs/2212.14052).
See our [GitHub](https://github.com/HazyResearch/H3) for instructions on how to download and run the model! |
BigDaddyNe1L/Hhaa | []
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} | 0 | null | ---
license: bsd-3-clause
---
# H3 Language Model
Official model weights for [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://arxiv.org/abs/2212.14052).
See our [GitHub](https://github.com/HazyResearch/H3) for instructions on how to download and run the model! |
BigSalmon/BertaMyWorda | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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}
} | 8 | null | ---
license: mit
tags:
- audio
- automatic-speech-recognition
- endpoints-template
library_name: generic
inference: false
---
# OpenAI [Whisper](https://github.com/openai/whisper) Inference Endpoint example
> Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
For more information about the model, license and limitations check the original repository at [openai/whisper](https://github.com/openai/whisper).
---
This repository implements a custom `handler` task for `automatic-speech-recognition` for 🤗 Inference Endpoints using OpenAIs new Whisper model. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/handler.py).
There is also a [notebook](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py`
### Request
The endpoint expects a binary audio file. Below is a cURL example and a Python example using the `requests` library.
**curl**
```bash
# load audio file
wget https://cdn-media.huggingface.co/speech_samples/sample1.flac
# run request
curl --request POST \
--url https://{ENDPOINT}/ \
--header 'Content-Type: audio/x-flac' \
--header 'Authorization: Bearer {HF_TOKEN}' \
--data-binary '@sample1.flac'
```
**Python**
```python
import json
from typing import List
import requests as r
import base64
import mimetypes
ENDPOINT_URL=""
HF_TOKEN=""
def predict(path_to_audio:str=None):
# read audio file
with open(path_to_audio, "rb") as i:
b = i.read()
# get mimetype
content_type= mimetypes.guess_type(path_to_audio)[0]
headers= {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": content_type
}
response = r.post(ENDPOINT_URL, headers=headers, data=b)
return response.json()
prediction = predict(path_to_audio="sample1.flac")
prediction
```
expected output
```json
{"text": " going along slushy country roads and speaking to damp audiences in draughty school rooms day after day for a fortnight. He'll have to put in an appearance at some place of worship on Sunday morning, and he can come to us immediately afterwards."}
```
|
BigSalmon/BlankSlots | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | {
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"T5ForConditionalGeneration"
],
"model_type": "t5",
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"prefix": "summarize: "
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"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 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### jatmikocooll Dreambooth model trained by fishers with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
BigSalmon/FormalBerta2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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} | 16 | null | ---
license: creativeml-openrail-m
language:
- en
tags:
- text-to-image
- stable-diffusion
---
A collection of anime models merged by me. Will update info and examples later. |
BigSalmon/InfillFormalLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 8 | 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: DoctorRobotnik/SnowballTarget-ppo-1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/InformalToFormalLincoln14 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 5 | 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: besa2001/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/InformalToFormalLincoln15 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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}
} | 11 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-mbert-ours-run-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hing-mbert-ours-run-2
This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3919
- Accuracy: 0.62
- Precision: 0.5759
- Recall: 0.5631
- F1: 0.5669
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0284 | 1.0 | 100 | 1.2914 | 0.595 | 0.5712 | 0.4800 | 0.4642 |
| 0.8127 | 2.0 | 200 | 0.8552 | 0.59 | 0.5744 | 0.5675 | 0.4891 |
| 0.5499 | 3.0 | 300 | 1.1212 | 0.645 | 0.6544 | 0.5600 | 0.5475 |
| 0.3433 | 4.0 | 400 | 1.2017 | 0.605 | 0.5872 | 0.5866 | 0.5791 |
| 0.2218 | 5.0 | 500 | 1.8329 | 0.655 | 0.6458 | 0.6064 | 0.6055 |
| 0.1763 | 6.0 | 600 | 2.4194 | 0.655 | 0.6140 | 0.5802 | 0.5871 |
| 0.1022 | 7.0 | 700 | 2.3894 | 0.66 | 0.6171 | 0.6045 | 0.6048 |
| 0.0631 | 8.0 | 800 | 2.8259 | 0.605 | 0.5704 | 0.5255 | 0.5259 |
| 0.0254 | 9.0 | 900 | 2.9135 | 0.65 | 0.6013 | 0.5734 | 0.5784 |
| 0.0316 | 10.0 | 1000 | 3.0548 | 0.62 | 0.5862 | 0.5650 | 0.5670 |
| 0.026 | 11.0 | 1100 | 3.1020 | 0.62 | 0.5722 | 0.5593 | 0.5619 |
| 0.0152 | 12.0 | 1200 | 3.0692 | 0.62 | 0.5685 | 0.5597 | 0.5621 |
| 0.0156 | 13.0 | 1300 | 3.1068 | 0.615 | 0.5771 | 0.5589 | 0.5624 |
| 0.0237 | 14.0 | 1400 | 3.3487 | 0.62 | 0.5924 | 0.5589 | 0.5642 |
| 0.0094 | 15.0 | 1500 | 3.2007 | 0.615 | 0.5665 | 0.5639 | 0.5650 |
| 0.0054 | 16.0 | 1600 | 3.2838 | 0.62 | 0.5807 | 0.5657 | 0.5690 |
| 0.005 | 17.0 | 1700 | 3.2258 | 0.615 | 0.5846 | 0.5723 | 0.5747 |
| 0.005 | 18.0 | 1800 | 3.3572 | 0.63 | 0.5827 | 0.5698 | 0.5736 |
| 0.0022 | 19.0 | 1900 | 3.3642 | 0.62 | 0.5759 | 0.5631 | 0.5669 |
| 0.0019 | 20.0 | 2000 | 3.3919 | 0.62 | 0.5759 | 0.5631 | 0.5669 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
BigSalmon/InformalToFormalLincoln16 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 8 | null | VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter
Yida Mu, Mali Jin, Charlie Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song
Accepted @ICWSM 2023
```bibtex
@article{mu2023vaxxhesitancy,
title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter},
author={Yida Mu and Mali Jin and Charlie Grimshaw and Carolina Scarton and Kalina Bontcheva and Xingyi Song},
year={2023},
eprint={2301.06660},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
license: mit
---
|
BigSalmon/InformalToFormalLincoln17 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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}
}
} | 12 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
# 『Plum Mix & Pear Mix』
<img src="https://i.imgur.com/uHMJpbH.png" width="1024" height="">
<img src="https://i.imgur.com/x5Aa0ly.jpg" width="1024" height="">
- "Plum Mix" is a model based on "AbyssOrangeMix2"(https://huggingface.co/WarriorMama777/OrangeMixs#abyssorangemix2) with hierarchical merging.
- "Pear Mix" is a realistic model based on "Plum Mix" adjusted by hierarchical merging.
----
# ◆Discord
[Join Discord Server](https://discord.gg/eN6aSWRddT)
- The merged model community of Hemlok.
----
# ◆About
- It is adjusted to produce a dark and lovely illustration.
- If you prefer a more realistic look, use the "Pear mix" model.
- Sampler: DDIM or DPM++ SDE Karras
- Steps: 50~
- Clipskip: 2
- CFG Scale: 5-8
- Denoise strength: 0.4-0.7(As you like)
- Negative prompts should be as few as possible.
- vae: As you wish. (Any etc. If not used, color may become lighter)
----
# ◆How to use
- Please download the file by yourself and use it with WebUI(AUTOMATIC1111) etc.
- Use the f16 version for Colab(T4) or a PC with low RAM.
----
# ◆Colab Note
[](https://colab.research.google.com/drive/1ajz2sZdClKTyNt145UTlKG3Vh6cq_7QS?usp=sharing)
- (I have not checked the operation but it probably works.)
----
# ◆Comparison
<img src="https://i.imgur.com/VMvsiIH.png" width="1700" height="">
<img src="https://i.imgur.com/2fQkoJU.png" width="1700" height="">
```
(masterpiece:1.2), (best quality:1.2), winter, (morning), (school), 1girl, solo, looking at viewer, cowboy shot, cardigan, (school uniform), smile, blonde hair, (knee socks), bag,
```
----
<img src="https://i.imgur.com/XvKhDVr.png" width="1700" height="">
<img src="https://i.imgur.com/jSZqx7X.png" width="1700" height="">
```
(masterpiece_1.2), (best quality_1.2), (1girl), (cowboy shot), (solo), (twintail), long hair, (school uniform), looking at viewe
```
----
# ◆Sampler & CFG Scale
## 『Plum Mix』
<img src="https://i.imgur.com/aU0jngA.jpg" width="1700" height="">
<img src="https://i.imgur.com/2CLd0Zw.jpg" width="1700" height="">
```
(masterpiece:1.2), (best quality:1.2), kawaii, winter, ((street)), ((building)), (noon), 1girl, solo, looking at viewer, ((maid uniform)), (twintails), long hair, blonde hair, smile, [small breast], shiny skin,
```
----
## 『Pear Mix』
<img src="https://i.imgur.com/njWmOqs.jpg" width="1700" height="">
<img src="https://i.imgur.com/DFGiAFm.jpg" width="1700" height="">
```
(masterpiece:1.2), (best quality:1.2), kawaii, winter, ((street)), ((building)), (noon), 1girl, solo, looking at viewer, ((maid uniform)), (twintails), long hair, blonde hair, smile, [small breast], shiny skin,
```
----
# Disclaimer
- The creation of SFW and NSFW images is at the discretion of the individual creator.
- This model is not a model created to publish NSFW content in public places, etc.
----
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
(Full text of the license: https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
BigSalmon/InformalToFormalLincoln20 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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} | 8 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-demo
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** Lake **Demo**
Q learning agent demo.
.
## Usage
```python
model = load_from_hub(repo_id="cinnabun/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"])
```
|
BigSalmon/InformalToFormalLincoln23 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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}
} | 5 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: DoctorRobotnik/Pyramids-ppo-1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/InformalToFormalLincoln24 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 5 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-mbert-ours-run-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. -->
# hing-mbert-ours-run-3
This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9769
- Accuracy: 0.675
- Precision: 0.6433
- Recall: 0.6344
- F1: 0.6344
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9089 | 1.0 | 100 | 1.0993 | 0.635 | 0.6487 | 0.5304 | 0.5060 |
| 0.6657 | 2.0 | 200 | 0.8138 | 0.645 | 0.6550 | 0.6482 | 0.6234 |
| 0.3858 | 3.0 | 300 | 1.1334 | 0.665 | 0.6162 | 0.6061 | 0.5995 |
| 0.208 | 4.0 | 400 | 1.9041 | 0.685 | 0.6488 | 0.6169 | 0.6087 |
| 0.0996 | 5.0 | 500 | 2.3735 | 0.645 | 0.5867 | 0.5781 | 0.5794 |
| 0.0296 | 6.0 | 600 | 2.5772 | 0.665 | 0.6284 | 0.6208 | 0.6198 |
| 0.0623 | 7.0 | 700 | 2.8906 | 0.655 | 0.6040 | 0.5916 | 0.5926 |
| 0.0395 | 8.0 | 800 | 2.6567 | 0.675 | 0.6279 | 0.6254 | 0.6219 |
| 0.029 | 9.0 | 900 | 2.9277 | 0.655 | 0.6208 | 0.5950 | 0.5991 |
| 0.0194 | 10.0 | 1000 | 2.7362 | 0.665 | 0.6241 | 0.6208 | 0.6190 |
| 0.0092 | 11.0 | 1100 | 2.5561 | 0.68 | 0.6396 | 0.6401 | 0.6385 |
| 0.0059 | 12.0 | 1200 | 3.1112 | 0.675 | 0.6350 | 0.5967 | 0.6042 |
| 0.0133 | 13.0 | 1300 | 2.5269 | 0.685 | 0.6520 | 0.6607 | 0.6519 |
| 0.0051 | 14.0 | 1400 | 2.8736 | 0.68 | 0.6381 | 0.6158 | 0.6134 |
| 0.0044 | 15.0 | 1500 | 2.9132 | 0.675 | 0.6336 | 0.6180 | 0.6200 |
| 0.0029 | 16.0 | 1600 | 2.8701 | 0.675 | 0.6337 | 0.6214 | 0.6233 |
| 0.0015 | 17.0 | 1700 | 2.8115 | 0.69 | 0.6475 | 0.6388 | 0.6420 |
| 0.0019 | 18.0 | 1800 | 2.9517 | 0.67 | 0.6373 | 0.6276 | 0.6273 |
| 0.0013 | 19.0 | 1900 | 2.9633 | 0.67 | 0.6373 | 0.6276 | 0.6273 |
| 0.0007 | 20.0 | 2000 | 2.9769 | 0.675 | 0.6433 | 0.6344 | 0.6344 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
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} | 9 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-mbert-ours-run-4
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. -->
# hing-mbert-ours-run-4
This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0173
- Accuracy: 0.68
- Precision: 0.6330
- Recall: 0.6325
- F1: 0.6320
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9781 | 1.0 | 100 | 0.8852 | 0.55 | 0.4044 | 0.5284 | 0.4211 |
| 0.7568 | 2.0 | 200 | 0.8110 | 0.655 | 0.5994 | 0.6013 | 0.5762 |
| 0.5121 | 3.0 | 300 | 0.9735 | 0.65 | 0.6145 | 0.6131 | 0.5965 |
| 0.314 | 4.0 | 400 | 1.1324 | 0.65 | 0.6305 | 0.6355 | 0.6266 |
| 0.1298 | 5.0 | 500 | 2.8247 | 0.61 | 0.5804 | 0.5087 | 0.5092 |
| 0.1013 | 6.0 | 600 | 2.8183 | 0.635 | 0.6212 | 0.5674 | 0.5667 |
| 0.0989 | 7.0 | 700 | 2.3235 | 0.635 | 0.5944 | 0.5922 | 0.5916 |
| 0.0481 | 8.0 | 800 | 2.5240 | 0.68 | 0.6334 | 0.6172 | 0.6221 |
| 0.018 | 9.0 | 900 | 2.6782 | 0.65 | 0.6123 | 0.6054 | 0.6062 |
| 0.0285 | 10.0 | 1000 | 2.3400 | 0.67 | 0.6206 | 0.6327 | 0.6189 |
| 0.014 | 11.0 | 1100 | 2.6558 | 0.66 | 0.6098 | 0.5992 | 0.6018 |
| 0.0085 | 12.0 | 1200 | 2.9366 | 0.66 | 0.6076 | 0.5961 | 0.5991 |
| 0.0106 | 13.0 | 1300 | 2.8567 | 0.665 | 0.6198 | 0.6193 | 0.6186 |
| 0.0097 | 14.0 | 1400 | 3.1526 | 0.64 | 0.6089 | 0.5975 | 0.5954 |
| 0.0022 | 15.0 | 1500 | 2.7305 | 0.69 | 0.6404 | 0.6404 | 0.6398 |
| 0.0016 | 16.0 | 1600 | 2.7670 | 0.69 | 0.6418 | 0.6434 | 0.6425 |
| 0.0017 | 17.0 | 1700 | 2.8193 | 0.7 | 0.6533 | 0.6566 | 0.6546 |
| 0.0009 | 18.0 | 1800 | 2.9959 | 0.685 | 0.6400 | 0.6389 | 0.6384 |
| 0.0006 | 19.0 | 1900 | 3.0153 | 0.68 | 0.6330 | 0.6325 | 0.6320 |
| 0.0005 | 20.0 | 2000 | 3.0173 | 0.68 | 0.6330 | 0.6325 | 0.6320 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
BigSalmon/MrLincoln5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 9 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: ScrappyCoco666/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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"GPT2LMHeadModel"
],
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} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- pubmed_qa
model-index:
- name: pubmedqa_roberta
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. -->
# pubmedqa_roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the pubmed_qa dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 200
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.79 | 2 | 1.0976 | 0.552 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.11.0
|
BigSalmon/MrLincoln7 | []
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} | 0 | null | ---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- PoseyATX/autotrain-data-foxhunterirontesting
co2_eq_emissions:
emissions: 25.447577064303335
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 2874884135
- CO2 Emissions (in grams): 25.4476
## Validation Metrics
- Loss: 1.027
- Rouge1: 60.232
- Rouge2: 42.909
- RougeL: 47.915
- RougeLsum: 54.128
- Gen Len: 193.351
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/PoseyATX/autotrain-foxhunterirontesting-2874884135
``` |
BigSalmon/ParaphraseParentheses2.0 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | {
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} | 13 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-mbert-ours-run-5
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. -->
# hing-mbert-ours-run-5
This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2437
- Accuracy: 0.665
- Precision: 0.6223
- Recall: 0.5991
- F1: 0.6039
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9643 | 1.0 | 100 | 0.7996 | 0.69 | 0.6596 | 0.6593 | 0.6521 |
| 0.6951 | 2.0 | 200 | 1.0464 | 0.66 | 0.6597 | 0.5831 | 0.5734 |
| 0.4245 | 3.0 | 300 | 0.9640 | 0.64 | 0.6025 | 0.6033 | 0.6010 |
| 0.238 | 4.0 | 400 | 1.6744 | 0.68 | 0.7095 | 0.6445 | 0.6359 |
| 0.1477 | 5.0 | 500 | 1.7115 | 0.665 | 0.6362 | 0.6422 | 0.6360 |
| 0.1206 | 6.0 | 600 | 2.0459 | 0.635 | 0.5749 | 0.5752 | 0.5726 |
| 0.0528 | 7.0 | 700 | 2.5698 | 0.66 | 0.6230 | 0.5904 | 0.5985 |
| 0.0525 | 8.0 | 800 | 2.2729 | 0.625 | 0.5741 | 0.5860 | 0.5733 |
| 0.0174 | 9.0 | 900 | 2.6227 | 0.635 | 0.6099 | 0.6044 | 0.6019 |
| 0.0088 | 10.0 | 1000 | 2.8854 | 0.63 | 0.5699 | 0.5676 | 0.5680 |
| 0.0085 | 11.0 | 1100 | 3.2173 | 0.655 | 0.6043 | 0.5771 | 0.5821 |
| 0.0121 | 12.0 | 1200 | 3.1270 | 0.665 | 0.6214 | 0.5903 | 0.5971 |
| 0.0141 | 13.0 | 1300 | 2.6648 | 0.655 | 0.5981 | 0.5978 | 0.5961 |
| 0.0116 | 14.0 | 1400 | 3.1711 | 0.665 | 0.6192 | 0.5915 | 0.5971 |
| 0.007 | 15.0 | 1500 | 3.0954 | 0.66 | 0.6156 | 0.5961 | 0.6009 |
| 0.0037 | 16.0 | 1600 | 3.3065 | 0.65 | 0.6027 | 0.5791 | 0.5824 |
| 0.0031 | 17.0 | 1700 | 3.1715 | 0.665 | 0.6177 | 0.5999 | 0.6048 |
| 0.0021 | 18.0 | 1800 | 3.1602 | 0.665 | 0.6220 | 0.6029 | 0.6082 |
| 0.0021 | 19.0 | 1900 | 3.2027 | 0.655 | 0.6096 | 0.5893 | 0.5937 |
| 0.0018 | 20.0 | 2000 | 3.2437 | 0.665 | 0.6223 | 0.5991 | 0.6039 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
|
BigTooth/Megumin-v0.2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-mnli-target-glue-mnli
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. -->
# small-mlm-glue-mnli-target-glue-mnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6497
- Accuracy: 0.7259
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9145 | 0.04 | 500 | 0.8234 | 0.6373 |
| 0.8123 | 0.08 | 1000 | 0.7786 | 0.6628 |
| 0.7745 | 0.12 | 1500 | 0.7489 | 0.6756 |
| 0.7496 | 0.16 | 2000 | 0.7311 | 0.6878 |
| 0.7424 | 0.2 | 2500 | 0.7205 | 0.6921 |
| 0.7325 | 0.24 | 3000 | 0.7007 | 0.7007 |
| 0.7126 | 0.29 | 3500 | 0.6780 | 0.7131 |
| 0.7007 | 0.33 | 4000 | 0.6652 | 0.7189 |
| 0.6755 | 0.37 | 4500 | 0.6737 | 0.7249 |
| 0.6803 | 0.41 | 5000 | 0.6497 | 0.7259 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 10 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: SergejSchweizer/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BillelBenoudjit/jplu-wikiann | [
"fr",
"dataset:wikiann",
"model-index"
]
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-rte-target-glue-mnli
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. -->
# small-mlm-glue-rte-target-glue-mnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6463
- Accuracy: 0.7267
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9151 | 0.04 | 500 | 0.8278 | 0.6394 |
| 0.8113 | 0.08 | 1000 | 0.7771 | 0.6612 |
| 0.7745 | 0.12 | 1500 | 0.7405 | 0.6781 |
| 0.7452 | 0.16 | 2000 | 0.7276 | 0.6896 |
| 0.742 | 0.2 | 2500 | 0.7132 | 0.6936 |
| 0.7305 | 0.24 | 3000 | 0.7041 | 0.6999 |
| 0.7128 | 0.29 | 3500 | 0.6800 | 0.7152 |
| 0.7019 | 0.33 | 4000 | 0.6703 | 0.7200 |
| 0.6772 | 0.37 | 4500 | 0.6758 | 0.7234 |
| 0.679 | 0.41 | 5000 | 0.6463 | 0.7267 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Bilz/DialoGPT-small-harrypotter | []
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} | 0 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hing-roberta-NCM-run-1
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. -->
# hing-roberta-NCM-run-1
This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2912
- Accuracy: 0.6667
- Precision: 0.6513
- Recall: 0.6494
- F1: 0.6502
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8968 | 1.0 | 927 | 0.8552 | 0.6257 | 0.6508 | 0.5961 | 0.5969 |
| 0.7022 | 2.0 | 1854 | 1.1142 | 0.3937 | 0.3270 | 0.3273 | 0.2051 |
| 0.5569 | 3.0 | 2781 | 0.9130 | 0.6591 | 0.6566 | 0.6612 | 0.6509 |
| 0.363 | 4.0 | 3708 | 1.6630 | 0.6526 | 0.6634 | 0.6414 | 0.6436 |
| 0.2801 | 5.0 | 4635 | 2.0458 | 0.6451 | 0.6339 | 0.6345 | 0.6330 |
| 0.1925 | 6.0 | 5562 | 2.3378 | 0.6570 | 0.6439 | 0.6254 | 0.6277 |
| 0.1297 | 7.0 | 6489 | 2.5205 | 0.6839 | 0.6719 | 0.6651 | 0.6675 |
| 0.114 | 8.0 | 7416 | 2.8373 | 0.6505 | 0.6379 | 0.6249 | 0.6280 |
| 0.0994 | 9.0 | 8343 | 2.5358 | 0.6634 | 0.6539 | 0.6446 | 0.6474 |
| 0.0977 | 10.0 | 9270 | 2.8244 | 0.6537 | 0.6489 | 0.6210 | 0.6238 |
| 0.0623 | 11.0 | 10197 | 2.7593 | 0.6764 | 0.6602 | 0.6487 | 0.6510 |
| 0.0537 | 12.0 | 11124 | 2.9823 | 0.6677 | 0.6679 | 0.6450 | 0.6488 |
| 0.0432 | 13.0 | 12051 | 3.0792 | 0.6537 | 0.6465 | 0.6352 | 0.6378 |
| 0.0406 | 14.0 | 12978 | 3.0707 | 0.6688 | 0.6592 | 0.6509 | 0.6534 |
| 0.0296 | 15.0 | 13905 | 3.3289 | 0.6667 | 0.6596 | 0.6452 | 0.6486 |
| 0.0288 | 16.0 | 14832 | 3.2147 | 0.6645 | 0.6592 | 0.6512 | 0.6528 |
| 0.024 | 17.0 | 15759 | 3.3284 | 0.6645 | 0.6470 | 0.6405 | 0.6425 |
| 0.0201 | 18.0 | 16686 | 3.2428 | 0.6688 | 0.6515 | 0.6515 | 0.6515 |
| 0.0176 | 19.0 | 17613 | 3.2680 | 0.6710 | 0.6574 | 0.6536 | 0.6547 |
| 0.0168 | 20.0 | 18540 | 3.2912 | 0.6667 | 0.6513 | 0.6494 | 0.6502 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Biniam/en_ti_translate | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
]
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} | 14 | 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: tmilushev/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
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} | 12 | 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: 33.50 +/- 19.05
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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
BinksSachary/ShaxxBot2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: detr-resnet-50_finetuned_cppe5
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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
Step Training Loss
300 2.162200
600 2.011000
1200 1.779500
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Blabla/Pipipopo | []
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 615.50 +/- 223.38
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 mmontecino -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 mmontecino -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 mmontecino
```
## 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)])
```
|
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
]
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} | 36 | null | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kejian/codeparrot-train-more-filter-3.3b-cleaned
model-index:
- name: downy-conditional
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. -->
# downy-conditional
This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned 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: 0.0008
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.1,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0},
'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'],
'is_split_by_sentences': True},
'generation': {'batch_size': 128,
'metrics_configs': [{}, {'n': 1}, {}],
'scenario_configs': [{'display_as_html': True,
'generate_kwargs': {'bad_words_ids': [[32769]],
'do_sample': True,
'eos_token_id': 0,
'max_length': 640,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_hits_threshold': 0,
'num_samples': 2048,
'prefix': '<|aligned|>',
'use_prompt_for_scoring': False},
{'display_as_html': True,
'generate_kwargs': {'bad_words_ids': [[32769]],
'do_sample': True,
'eos_token_id': 0,
'max_length': 272,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'functions',
'num_hits_threshold': 0,
'num_samples': 2048,
'prefix': '<|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/functions_csnet.jsonl',
'use_prompt_for_scoring': True}],
'scorer_config': {}},
'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>',
'should_insert_prefix': True},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'codeparrot/codeparrot-small'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'downy-conditional',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0008,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000.0,
'output_dir': 'training_output',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25177,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/yfg3xv7g |
Blerrrry/Kkk | []
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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: 243.93 +/- 38.79
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
...
```
|
BlightZz/MakiseKurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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"GPT2LMHeadModel"
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} | 14 | null | ---
license: mit
---
# Diffusion Model Trained on Food-101 Dataset
```python
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("chenchaozhao/ddpm-food101-128")
```
 |
Bloodwarrior/Chikfalay | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: tmilushev/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Botjallu/DialoGPT-small-harrypotter | []
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} | 0 | 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="asarvazyan/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"])
```
|
Brayan/CNN_Brain_Tumor | []
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi
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="asarvazyan/q-Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Brona/model1 | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: small-mlm-glue-mnli-target-glue-mrpc
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. -->
# small-mlm-glue-mnli-target-glue-mrpc
This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9598
- Accuracy: 0.7721
- F1: 0.8432
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3946 | 4.35 | 500 | 0.8309 | 0.7647 | 0.8476 |
| 0.0715 | 8.7 | 1000 | 1.3662 | 0.7647 | 0.8405 |
| 0.0285 | 13.04 | 1500 | 1.6659 | 0.7647 | 0.8405 |
| 0.0149 | 17.39 | 2000 | 1.8421 | 0.7696 | 0.8396 |
| 0.0158 | 21.74 | 2500 | 1.9587 | 0.7647 | 0.8426 |
| 0.0152 | 26.09 | 3000 | 2.0488 | 0.7672 | 0.848 |
| 0.0147 | 30.43 | 3500 | 1.9463 | 0.7770 | 0.8535 |
| 0.0096 | 34.78 | 4000 | 1.7938 | 0.7819 | 0.8529 |
| 0.0124 | 39.13 | 4500 | 1.8361 | 0.7868 | 0.8538 |
| 0.0152 | 43.48 | 5000 | 1.9598 | 0.7721 | 0.8432 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Brona/poc_de | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: small-mlm-glue-rte-target-glue-mrpc
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. -->
# small-mlm-glue-rte-target-glue-mrpc
This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5950
- Accuracy: 0.8015
- F1: 0.8639
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3827 | 4.35 | 500 | 0.7389 | 0.7819 | 0.8558 |
| 0.0662 | 8.7 | 1000 | 1.0391 | 0.8039 | 0.8635 |
| 0.023 | 13.04 | 1500 | 1.4380 | 0.7990 | 0.8629 |
| 0.0221 | 17.39 | 2000 | 1.5043 | 0.7966 | 0.8605 |
| 0.0191 | 21.74 | 2500 | 1.5950 | 0.8015 | 0.8639 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
BrunoNogueira/DialoGPT-kungfupanda | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 10 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 32.30 +/- 16.27
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Brunomezenga/NN | []
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### companioncube Dreambooth model trained by Wusul 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:
|
Bryan190/Aguy190 | []
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} | 0 | null | ---
license: apache-2.0
datasets:
- NbAiLab/balanced_bokmaal_nynorsk
language:
- nn
- 'no'
- nb
pipeline_tag: translation
metrics:
- bleu
---
# Model Card for Model ID
This model is a long sequence version of a finetuned version of [north/t5_base_NCC_modern](https://huggingface.co/north/t5_base_NCC_modern).
| | Size |Model|BLEU|
|:------------:|:------------:|:------------:|:------------:|
|Small |_60M_|[🤗](https://huggingface.co/north/nynorsk_North_small_long)|93.55|
|**Base** |**_220M_**|✔|**94.03**|
|Large |_770M_|[🤗](https://huggingface.co/north/nynorsk_North_large_long)|94.12|
# Model Details
Please see the model card for the base model for more information. |
Bryanwong/wangchanberta-ner | []
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} | 0 | null | ---
license: apache-2.0
datasets:
- NbAiLab/balanced_bokmaal_nynorsk
language:
- nn
- 'no'
- nb
metrics:
- bleu
pipeline_tag: translation
---
# Model Card for Model ID
This model is a long sequence version of a finetuned version of [north/t5_small_NCC_modern](https://huggingface.co/north/t5_small_NCC_modern).
| | Size |Model|BLEU|
|:------------:|:------------:|:------------:|:------------:|
|**Small** |**_60M_**|✔|**93.55**|
|Base |_220M_|[🤗](https://huggingface.co/north/nynorsk_North_base_long)|94.03|
|Large |_770M_|[🤗](https://huggingface.co/north/nynorsk_North_large_long)|94.12|
# Model Details
Please see the model card for the base model for more information. |
Brykee/BrykeeBot | []
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} | 0 | null | ---
license: apache-2.0
datasets:
- NbAiLab/balanced_bokmaal_nynorsk
language:
- nn
- 'no'
- nb
metrics:
- bleu
pipeline_tag: translation
---
# Model Card for Model ID
This model is a long sequence verison of a finetuned version of [north/t5_large_NCC_modern](https://huggingface.co/north/t5_large_NCC_modern).
| | Size |Model|BLEU|
|:------------:|:------------:|:------------:|:------------:|
|Small |_60M_|[🤗](https://huggingface.co/north/nynorsk_North_small_long)|93.55|
|Base |_220M_|[🤗](https://huggingface.co/north/nynorsk_North_base_long)|94.03|
|**Large** |**_770M_**|✔|**94.12**|
# Model Details
Please see the model card for the base model for more information. |
Brykee/DialoGPT-medium-Morty | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 10 | 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="marcov/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"])
```
|
Bryson575x/riceboi | []
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.42 +/- 2.67
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="marcov/q-taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BunakovD/sd | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-rte-target-glue-qnli
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. -->
# small-mlm-glue-rte-target-glue-qnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3460
- Accuracy: 0.8565
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.484 | 0.15 | 500 | 0.3909 | 0.8314 |
| 0.4419 | 0.31 | 1000 | 0.3811 | 0.8373 |
| 0.4211 | 0.46 | 1500 | 0.3588 | 0.8459 |
| 0.4101 | 0.61 | 2000 | 0.3677 | 0.8384 |
| 0.4092 | 0.76 | 2500 | 0.3361 | 0.8528 |
| 0.3922 | 0.92 | 3000 | 0.3280 | 0.8567 |
| 0.3499 | 1.07 | 3500 | 0.3402 | 0.8587 |
| 0.3163 | 1.22 | 4000 | 0.3374 | 0.8622 |
| 0.3145 | 1.37 | 4500 | 0.3471 | 0.8565 |
| 0.3202 | 1.53 | 5000 | 0.3460 | 0.8565 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Bwehfuk/Ron | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-mnli-target-glue-qnli
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. -->
# small-mlm-glue-mnli-target-glue-qnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3505
- Accuracy: 0.8576
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.487 | 0.15 | 500 | 0.3918 | 0.8303 |
| 0.4456 | 0.31 | 1000 | 0.3872 | 0.8312 |
| 0.423 | 0.46 | 1500 | 0.3588 | 0.8464 |
| 0.4118 | 0.61 | 2000 | 0.3692 | 0.8360 |
| 0.412 | 0.76 | 2500 | 0.3426 | 0.8514 |
| 0.3945 | 0.92 | 3000 | 0.3313 | 0.8530 |
| 0.3544 | 1.07 | 3500 | 0.3423 | 0.8578 |
| 0.3167 | 1.22 | 4000 | 0.3529 | 0.8570 |
| 0.3152 | 1.37 | 4500 | 0.3512 | 0.8569 |
| 0.3228 | 1.53 | 5000 | 0.3505 | 0.8576 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CALM/backup | [
"lean_albert",
"transformers"
]
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} | 4 | null | ---
license: mit
---
# PromCSE: Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning
[](https://colab.research.google.com/drive/1lanXViJzbmGM1bwm8AflNUKmrvDidg_3?usp=sharing)
arXiv link: https://arxiv.org/abs/2203.06875v2
Published in [**EMNLP 2022**](https://2022.emnlp.org/)
Our code is modified based on [SimCSE](https://github.com/princeton-nlp/SimCSE) and [P-tuning v2](https://github.com/THUDM/P-tuning-v2/). Here we would like to sincerely thank them for their excellent works.
## Model List
We have released our supervised and unsupervised models on huggingface, which acquire **Top 1** results on 1 domain-shifted STS task and 4 standard STS tasks:
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-cxc?p=deep-continuous-prompt-for-contrastive-1)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sick?p=deep-continuous-prompt-for-contrastive-1)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts12?p=deep-continuous-prompt-for-contrastive-1)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts13?p=deep-continuous-prompt-for-contrastive-1)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts14?p=deep-continuous-prompt-for-contrastive-1)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts16?p=deep-continuous-prompt-for-contrastive-1)
[](https://paperswithcode.com/sota/semantic-textual-similarity-on-sts15?p=deep-continuous-prompt-for-contrastive-1)
<!-- <img src="https://github.com/YJiangcm/DCPCSE/blob/master/figure/leaderboard.png" width="700" height="380"> -->
| Model | STS12 | STS13 | STS14 | STS15 | STS16 | STS-B | SICK-R | Avg. |
|:-----------------------:|:-----:|:----------:|:---------:|:-----:|:-----:|:-----:|:-----:|:-----:|
| [YuxinJiang/unsup-promcse-bert-base-uncased](https://huggingface.co/YuxinJiang/unsup-promcse-bert-base-uncased) | 73.03 |85.18| 76.70| 84.19 |79.69| 80.62| 70.00| 78.49|
| [YuxinJiang/sup-promcse-roberta-base](https://huggingface.co/YuxinJiang/sup-promcse-roberta-base) | 76.75 |85.86| 80.98| 86.51 |83.51| 86.58| 80.41| 82.94|
| [YuxinJiang/sup-promcse-roberta-large](https://huggingface.co/YuxinJiang/sup-promcse-roberta-large) | 79.14 |88.64| 83.73| 87.33 |84.57| 87.84| 82.07| 84.76|
**Naming rules**: `unsup` and `sup` represent "unsupervised" (trained on Wikipedia corpus) and "supervised" (trained on NLI datasets) respectively.
## Usage
[](https://colab.research.google.com/drive/1lanXViJzbmGM1bwm8AflNUKmrvDidg_3?usp=sharing)
We provide an easy-to-use python package `promcse` which contains the following functions:
**(1) encode sentences into embedding vectors;
(2) compute cosine simiarities between sentences;
(3) given queries, retrieval top-k semantically similar sentences for each query.**
To use the tool, first install the `promcse` package from [PyPI](https://pypi.org/project/promcse/)
```bash
pip install promcse
```
After installing the package, you can load our model by two lines of code
```python
from promcse import PromCSE
model = PromCSE("YuxinJiang/unsup-promcse-bert-base-uncased", "cls_before_pooler", 16)
# model = PromCSE("YuxinJiang/sup-promcse-roberta-base")
# model = PromCSE("YuxinJiang/sup-promcse-roberta-large")
```
Then you can use our model for **encoding sentences into embeddings**
```python
embeddings = model.encode("A woman is reading.")
```
**Compute the cosine similarities** between two groups of sentences
```python
sentences_a = ['A woman is reading.', 'A man is playing a guitar.']
sentences_b = ['He plays guitar.', 'A woman is making a photo.']
similarities = model.similarity(sentences_a, sentences_b)
```
Or build index for a group of sentences and **search** among them
```python
sentences = ['A woman is reading.', 'A man is playing a guitar.']
model.build_index(sentences)
results = model.search("He plays guitar.")
```
## Train PromCSE
In the following section, we describe how to train a PromCSE model by using our code.
### Setups
[](https://www.python.org/downloads/release/python-382/)
[](https://pytorch.org/get-started/previous-versions/)
Run the following script to install the remaining dependencies,
```bash
pip install -r requirements.txt
```
### Evaluation
[](https://colab.research.google.com/drive/1lanXViJzbmGM1bwm8AflNUKmrvDidg_3?usp=sharing)
Our evaluation code for sentence embeddings is based on a modified version of [SentEval](https://github.com/facebookresearch/SentEval). It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation. The STS tasks include seven standard STS tasks (STS12-16, STSB, SICK-R) and one domain-shifted STS task (CxC).
Before evaluation, please download the evaluation datasets by running
```bash
cd SentEval/data/downstream/
bash download_dataset.sh
```
To evaluate the domain shift robustness of sentence embedding, we need to download [CxC](https://drive.google.com/drive/folders/1ZnRlVlc4kFsKbaWj9cFbb8bQU0fxzz1c?usp=sharing), and put the data into *SentEval/data/downstream/CocoCXC*
Then come back to the root directory, you can evaluate the well trained models using our evaluation code. For example,
```bash
python evaluation.py \
--model_name_or_path YuxinJiang/sup-promcse-roberta-large \
--pooler_type cls \
--task_set sts \
--mode test \
--pre_seq_len 10
```
which is expected to output the results in a tabular format:
```
------ test ------
+-------+-------+-------+-------+-------+--------------+-----------------+-------+
| STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | Avg. |
+-------+-------+-------+-------+-------+--------------+-----------------+-------+
| 79.14 | 88.64 | 83.73 | 87.33 | 84.57 | 87.84 | 82.07 | 84.76 |
+-------+-------+-------+-------+-------+--------------+-----------------+-------+
```
Arguments for the evaluation script are as follows,
* `--model_name_or_path`: The name or path of a `transformers`-based pre-trained checkpoint.
* `--pooler_type`: Pooling method. Now we support
* `cls` (default): Use the representation of `[CLS]` token. A linear+activation layer is applied after the representation (it's in the standard BERT implementation). If you use **supervised PromCSE**, you should use this option.
* `cls_before_pooler`: Use the representation of `[CLS]` token without the extra linear+activation. If you use **unsupervised PromCSE**, you should take this option.
* `avg`: Average embeddings of the last layer. If you use checkpoints of SBERT/SRoBERTa ([paper](https://arxiv.org/abs/1908.10084)), you should use this option.
* `avg_top2`: Average embeddings of the last two layers.
* `avg_first_last`: Average embeddings of the first and last layers. If you use vanilla BERT or RoBERTa, this works the best.
* `--mode`: Evaluation mode
* `test` (default): The default test mode. To faithfully reproduce our results, you should use this option.
* `dev`: Report the development set results. Note that in STS tasks, only `STS-B` and `SICK-R` have development sets, so we only report their numbers. It also takes a fast mode for transfer tasks, so the running time is much shorter than the `test` mode (though numbers are slightly lower).
* `fasttest`: It is the same as `test`, but with a fast mode so the running time is much shorter, but the reported numbers may be lower (only for transfer tasks).
* `--task_set`: What set of tasks to evaluate on (if set, it will override `--tasks`)
* `sts` (default): Evaluate on STS tasks, including `STS 12~16`, `STS-B` and `SICK-R`. This is the most commonly-used set of tasks to evaluate the quality of sentence embeddings.
* `cococxc`: Evaluate on domain-shifted CXC task.
* `transfer`: Evaluate on transfer tasks.
* `full`: Evaluate on both STS and transfer tasks.
* `na`: Manually set tasks by `--tasks`.
* `--tasks`: Specify which dataset(s) to evaluate on. Will be overridden if `--task_set` is not `na`. See the code for a full list of tasks.
* `--pre_seq_len`: The length of deep continuous prompt.
### Training
**Data**
Following SimCSE, we use the same datasets to train our unsupervised models and supervised models. You can run `data/download_wiki.sh` and `data/download_nli.sh` to download the two datasets.
**Training scripts**
(The same as `run_unsup_example.sh`)
```bash
python train.py \
--model_name_or_path bert-base-uncased \
--train_file data/wiki1m_for_simcse.txt \
--output_dir result/my-unsup-promcse-bert-base-uncased \
--num_train_epochs 1 \
--per_device_train_batch_size 256 \
--learning_rate 3e-2 \
--max_seq_length 32 \
--evaluation_strategy steps \
--metric_for_best_model stsb_spearman \
--load_best_model_at_end \
--eval_steps 125 \
--pooler_type cls \
--mlp_only_train \
--pre_seq_len 16 \
--overwrite_output_dir \
--temp 0.05 \
--do_train \
--do_eval \
--fp16
```
We provide example training scripts for both unsupervised and supervised PromCSE. In `run_unsup_example.sh`, we provide a single-GPU (or CPU) example for the unsupervised version, and in `run_sup_example.sh` we give a **multiple-GPU** example for the supervised version. Both scripts call `train.py` for training. We explain the arguments in following:
* `--train_file`: Training file path. We support "txt" files (one line for one sentence) and "csv" files (2-column: pair data with no hard negative; 3-column: pair data with one corresponding hard negative instance). You can use our provided Wikipedia or NLI data, or you can use your own data with the same format.
* `--model_name_or_path`: Pre-trained checkpoints to start with. For now we support BERT-based models (`bert-base-uncased`, `bert-large-uncased`, etc.) and RoBERTa-based models (`RoBERTa-base`, `RoBERTa-large`, etc.).
* `--temp`: Temperature for the contrastive loss.
* `--pooler_type`: Pooling method. It's the same as the `--pooler_type` in the [evaluation part](#evaluation).
* `--mlp_only_train`: We have found that for unsupervised PromCSE, it works better to train the model with MLP layer but test the model without it. You should use this argument when training unsupervised PromCSE models.
* `--hard_negative_weight`: If using hard negatives (i.e., there are 3 columns in the training file), this is the logarithm of the weight. For example, if the weight is 1, then this argument should be set as 0 (default value).
* `--do_mlm`: Whether to use the MLM auxiliary objective. If True:
* `--mlm_weight`: Weight for the MLM objective.
* `--mlm_probability`: Masking rate for the MLM objective.
* `--pre_seq_len`: The length of deep continuous prompt.
* `--prefix_projection`: Whether apply a two-layer MLP head over the prompt embeddings.
* `--prefix_hidden_size`: The hidden size of the MLP projection head if prefix_projection is used.
* `--do_eh_loss`: Whether to use Energy-based Hinge loss in supervised models. If True:
* `--eh_loss_margin`: Margin of Energy-based Hinge loss.
* `--eh_loss_weight`: Weight of Energy-based Hinge loss.
All the other arguments are standard Huggingface's `transformers` training arguments. Some of the often-used arguments are: `--output_dir`, `--learning_rate`, `--per_device_train_batch_size`. In our example scripts, we also set to evaluate the model on the STS-B development set (need to download the dataset following the [evaluation](#evaluation) section) and save the best checkpoint.
All our experiments are conducted on Nvidia 3090 GPUs.
**Hyperparameters**
| **Unsupervised** | BERT-base | BERT-large | RoBERTa-base | RoBERTa-large |
|:--------------|:-----------:|:--------------:|:---------:|:---------:|
| Batch size | 256 | 256 | 64 | 64
| Learning rate | 3e-2 | 3e-2 | 3e-2 | 1e-2 |
| Prompt length | 16 | 10 | 14 | 10 |
| do_mlm | False | False | True | True |
| Epoch |1|1|1|1|
| Valid steps | 125 | 125 | 125 | 125 |
| **Supervised** | BERT-base | BERT-large | RoBERTa-base | RoBERTa-large |
|:--------------|:-----------:|:--------------:|:---------:|:---------:|
| Batch size | 256 | 256 | 512 | 512
| Learning rate | 1e-2 | 5e-3 | 1e-2 | 5e-3 |
| Prompt length | 12 | 12 | 10 | 10 |
| do_mlm | False | False | False | False |
| Epoch |10|10|10|10|
| Valid steps | 125 | 125 | 125 | 125 |
## Citation
Please cite our paper by:
```bibtex
@inproceedings{jiang-etal-2022-improved,
title = "Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning",
author = "Jiang, Yuxin and
Zhang, Linhan and
Wang, Wei",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.220",
pages = "3021--3035",
}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 16,451 | null | ---
library_name: stable-baselines3
tags:
- QbertNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: QbertNoFrameskip-v4
type: QbertNoFrameskip-v4
metrics:
- type: mean_reward
value: 11515.00 +/- 3534.97
name: mean_reward
verified: false
---
# **PPO** Agent playing **QbertNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **QbertNoFrameskip-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 QbertNoFrameskip-v4 -orga Qilex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env QbertNoFrameskip-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 QbertNoFrameskip-v4 -orga Qilex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env QbertNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env QbertNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env QbertNoFrameskip-v4 -f logs/ -orga Qilex
```
## 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-ca-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"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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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 18 | null | Access to model sd-concepts-library/george-bokhua-logo is restricted and you are not in the authorized list. Visit https://huggingface.co/sd-concepts-library/george-bokhua-logo to ask for access. |
CAMeL-Lab/bert-base-arabic-camelbert-ca-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": {
"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": {
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"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
}
}
} | 73 | null | ---
library_name: stable-baselines3
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: 20.30 +/- 1.00
name: mean_reward
verified: false
---
# **PPO** Agent playing **PongNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **PongNoFrameskip-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 PongNoFrameskip-v4 -orga Qilex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env PongNoFrameskip-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 PongNoFrameskip-v4 -orga Qilex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env PongNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env PongNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env PongNoFrameskip-v4 -f logs/ -orga Qilex
```
## 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-ca | [
"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,
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"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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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 580 | null | ---
license: creativeml-openrail-m
---
# Paper Cut Craft is a fine tuned Stable Diffusion model trained on Midjourney images
This is a fine-tuned SD 2.0 embedding trained on 768x768 resolution Midjourney images.
Include "papercutcraft-v2" in your prompt to get the effect of the embedding.
### Images
40 Steps:
<img src="https://huggingface.co/OlafII/papercutcraft-v2/resolve/main/images/grid-0041.png" width="100%"/>
<img src="https://huggingface.co/OlafII/papercutcraft-v2/resolve/main/images/grid-0042.png" width="100%"/>
<img src="https://huggingface.co/OlafII/papercutcraft-v2/resolve/main/images/grid-0043.png" width="100%"/>
### Training Info
Trained using Automatic1111 Textual Inverison Trainer
58 images at 15000 Steps |
CAMeL-Lab/bert-base-arabic-camelbert-da-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"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
}
}
} | 42 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 1012.50 +/- 368.47
name: mean_reward
verified: false
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -orga Qilex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -orga Qilex -f logs/
python -m rl_zoo3.enjoy --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Qilex
```
## 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-da-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
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} | 37 | null | ---
tags:
- Phoenix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Phoenix-v5
type: Phoenix-v5
metrics:
- type: mean_reward
value: 40165.00 +/- 25161.70
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Phoenix-v5**
This is a trained model of a PPO agent playing Phoenix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Phoenix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Phoenix-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Phoenix-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Phoenix-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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}
} | 32 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: small-mlm-glue-rte-target-glue-qqp
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. -->
# small-mlm-glue-rte-target-glue-qqp
This model is a fine-tuned version of [muhtasham/small-mlm-glue-rte](https://huggingface.co/muhtasham/small-mlm-glue-rte) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3294
- Accuracy: 0.8496
- F1: 0.8112
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4764 | 0.04 | 500 | 0.4288 | 0.7863 | 0.7498 |
| 0.4172 | 0.09 | 1000 | 0.3936 | 0.8089 | 0.7701 |
| 0.4017 | 0.13 | 1500 | 0.3707 | 0.8236 | 0.7785 |
| 0.3865 | 0.18 | 2000 | 0.3751 | 0.8197 | 0.7857 |
| 0.3788 | 0.22 | 2500 | 0.3682 | 0.8292 | 0.7938 |
| 0.364 | 0.26 | 3000 | 0.3517 | 0.8351 | 0.7969 |
| 0.3616 | 0.31 | 3500 | 0.3324 | 0.8496 | 0.8043 |
| 0.3533 | 0.35 | 4000 | 0.3348 | 0.8457 | 0.8071 |
| 0.3599 | 0.4 | 4500 | 0.3362 | 0.8451 | 0.8094 |
| 0.3465 | 0.44 | 5000 | 0.3294 | 0.8496 | 0.8112 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 54 | 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: 255.87 +/- 19.62
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
...
```
|
CAMeL-Lab/bert-base-arabic-camelbert-da | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"BertForMaskedLM"
],
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}
} | 449 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- pubmed_qa
model-index:
- name: pubmedqa_roberta_large
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. -->
# pubmedqa_roberta_large
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the pubmed_qa dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 25
- total_train_batch_size: 50
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 3 | 10 | 0.9957 | 0.552 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.11.0
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
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} | 45 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: imflash217/proximal_policy_optimization_huggy_unity
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 63 | null | ---
language: gsw
license: cc
---
# Swiss German STTS Part-of-Speech Tagging Model
The **swiss_german_pos_model** is a part-of-speech tagging model for Swiss German. The model is trained on [STTS POS Tags](https://universaldependencies.org/tagset-conversion/de-stts-uposf.html).
Note that there is also a model trained on [Universal POS tags (upos)](https://universaldependencies.org/u/pos/): [swiss_german_pos_model](https://huggingface.co/noeminaepli/swiss_german_pos_model).
### Training procedure and data sets
1) Base model: German LM: [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased)
2) Continued LM training with [swisscrawl data](https://icosys.ch/swisscrawl)
3) Task fine-tuning on the [UD\_German-HDT](https://github.com/UniversalDependencies/UD_German-HDT/tree/master) data set with [character-level noise](https://aclanthology.org/2022.findings-acl.321/)
4) Task fine-tuning on the Swiss German [NOAH-Corpus](https://noe-eva.github.io/NOAH-Corpus/) (train + dev split) & dev split of [UD\_German-HDT](https://github.com/UniversalDependencies/UD_German-HDT/tree/master)
- Accuracy on Swiss German NOAH test split: 0.9432
- Accuracy on German UD_German-HDT test set after GSW fine-tuning: 0.9826 (vs 0.9828 at step 3 before GSW fine-tuning)
### Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
model = AutoModelForTokenClassification.from_pretrained("noeminaepli/swiss_german_stts_pos_model")
tokenizer = AutoTokenizer.from_pretrained("noeminaepli/swiss_german_stts_pos_model")
pos_tagger = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
tokens = pos_tagger("Worum söu mes ned chönne?")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
### Citation
``` @inproceedings{aepli-sennrich-2022-improving,
title = "Improving Zero-Shot Cross-lingual Transfer Between Closely Related Languages by Injecting Character-Level Noise",
author = {Aepli, No{\"e}mi and
Sennrich, Rico},
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.321",
doi = "10.18653/v1/2022.findings-acl.321",
pages = "4074--4083",
abstract = "Cross-lingual transfer between a high-resource language and its dialects or closely related language varieties should be facilitated by their similarity. However, current approaches that operate in the embedding space do not take surface similarity into account. This work presents a simple yet effective strategy to improve cross-lingual transfer between closely related varieties. We propose to augment the data of the high-resource source language with character-level noise to make the model more robust towards spelling variations. Our strategy shows consistent improvements over several languages and tasks: Zero-shot transfer of POS tagging and topic identification between language varieties from the Finnic, West and North Germanic, and Western Romance language branches. Our work provides evidence for the usefulness of simple surface-level noise in improving transfer between language varieties.",
} ```
|
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"
]
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} | 1,860 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: small-mlm-glue-mnli-target-glue-qqp
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. -->
# small-mlm-glue-mnli-target-glue-qqp
This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3263
- Accuracy: 0.8535
- F1: 0.8134
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4778 | 0.04 | 500 | 0.4286 | 0.7863 | 0.7468 |
| 0.4182 | 0.09 | 1000 | 0.3862 | 0.8142 | 0.7696 |
| 0.4014 | 0.13 | 1500 | 0.3732 | 0.8225 | 0.7767 |
| 0.3851 | 0.18 | 2000 | 0.3686 | 0.8234 | 0.7887 |
| 0.3784 | 0.22 | 2500 | 0.3600 | 0.8338 | 0.7974 |
| 0.36 | 0.26 | 3000 | 0.3438 | 0.8406 | 0.7995 |
| 0.3583 | 0.31 | 3500 | 0.3361 | 0.8475 | 0.7970 |
| 0.3528 | 0.35 | 4000 | 0.3316 | 0.8472 | 0.8076 |
| 0.3567 | 0.4 | 4500 | 0.3307 | 0.8494 | 0.8089 |
| 0.3428 | 0.44 | 5000 | 0.3263 | 0.8535 | 0.8134 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Cameron/BERT-mdgender-convai-ternary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 38 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-mnli-target-glue-sst2
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. -->
# small-mlm-glue-mnli-target-glue-sst2
This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4163
- Accuracy: 0.8876
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3946 | 0.24 | 500 | 0.3598 | 0.8406 |
| 0.3068 | 0.48 | 1000 | 0.3467 | 0.8383 |
| 0.2583 | 0.71 | 1500 | 0.3512 | 0.8612 |
| 0.2412 | 0.95 | 2000 | 0.3227 | 0.8693 |
| 0.1855 | 1.19 | 2500 | 0.3500 | 0.8739 |
| 0.1701 | 1.43 | 3000 | 0.3967 | 0.8589 |
| 0.1664 | 1.66 | 3500 | 0.3699 | 0.875 |
| 0.1718 | 1.9 | 4000 | 0.3309 | 0.8922 |
| 0.134 | 2.14 | 4500 | 0.4765 | 0.8624 |
| 0.1229 | 2.38 | 5000 | 0.4163 | 0.8876 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Cameron/BERT-rtgender-opgender-annotations | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
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} | 33 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: illustration of a panda animal sitting on top of the deck of a battle ship
traveling through the open sea with a lot of ships surrounding it
---
# DreamBooth model for the panda concept trained by zhangshengdong.
This is a Stable Diffusion model fine-tuned on the panda concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of panda animal**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `animal-panda` images for the animal theme,
for the Hugging Face DreamBooth Hackathon, from the HF CN Community,
corporated with the HeyWhale.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('zhangshengdong/panda-animal-zsd-heywhale')
image = pipeline().images[0]
image
```
|
Camzure/MaamiBot | []
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} | 0 | null | ---
license: creativeml-openrail-m
pipeline_tag: text-to-image
---
### a Better model is out, Go to https://huggingface.co/no3/kat-at3-beta1
### kat from [Flipon](https://store.steampowered.com/app/1285020/Flipon/) on [WD](https://huggingface.co/hakurei/waifu-diffusion) via Dreambooth
#### model by no3
This your waifu-diffusion v1.4 model fine-tuned kat concept taught to waifu-diffusion v1.4 with Dreambooth.
It can be used by modifying the `instance_prompt`: **sks_kaatt**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts).
### note
If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download one or more file from here for your convenience.
[katFl-wd-1.4-beta1.ckpt](https://huggingface.co/no3/kat-wd-1.4-beta1/resolve/main/katFl-wd-1.4-beta1.ckpt) 5.16 GB
[katFl-wd-1.4-beta1-pruned.ckpt](https://huggingface.co/no3/kat-wd-1.4-beta1/resolve/main/katFl-wd-1.4-beta1-pruned.ckpt) 2.58 GB Uses less storage space, but untested yet
If you have issues or questions feel free to visit the Community Tab and start discussion about it.
Here are images used for training this concept:
 |
Canyonevo/DialoGPT-medium-KingHenry | []
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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: 278.37 +/- 16.34
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
...
```
|
Cdial/hausa-asr | [
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sst2
metrics:
- accuracy
model-index:
- name: bert-base-uncased-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sst2
type: sst2
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.876
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9312
- Accuracy: 0.876
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 1.0209 | 0.836 |
| No log | 2.0 | 250 | 1.0430 | 0.85 |
| No log | 3.0 | 375 | 0.9312 | 0.876 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-base-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
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} | 34 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: out
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. -->
# out
This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5164
- Accuracy: 0.6896
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 80
- total_train_batch_size: 2560
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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],
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} | 7 | 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: msgerasyov/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
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}
} | 27 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: msgerasyov/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
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} | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-sst2-target-glue-mnli
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. -->
# small-mlm-glue-sst2-target-glue-mnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6528
- Accuracy: 0.7271
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9063 | 0.04 | 500 | 0.8249 | 0.6370 |
| 0.8116 | 0.08 | 1000 | 0.7813 | 0.6619 |
| 0.7724 | 0.12 | 1500 | 0.7504 | 0.6764 |
| 0.7489 | 0.16 | 2000 | 0.7261 | 0.6908 |
| 0.7413 | 0.2 | 2500 | 0.7141 | 0.6900 |
| 0.7312 | 0.24 | 3000 | 0.7088 | 0.6972 |
| 0.7146 | 0.29 | 3500 | 0.6805 | 0.7127 |
| 0.7041 | 0.33 | 4000 | 0.6703 | 0.7164 |
| 0.6815 | 0.37 | 4500 | 0.6674 | 0.7241 |
| 0.6828 | 0.41 | 5000 | 0.6528 | 0.7271 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"BertForQuestionAnswering"
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} | 5 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-menelaos-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="menelaos/q-menelaos-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"])
```
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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}
} | 39 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3-menelaos
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="menelaos/q-taxi-v3-menelaos", 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"])
```
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
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}
} | 24 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-mrpc-target-glue-mnli
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. -->
# small-mlm-glue-mrpc-target-glue-mnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc](https://huggingface.co/muhtasham/small-mlm-glue-mrpc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6541
- Accuracy: 0.7253
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9151 | 0.04 | 500 | 0.8235 | 0.6375 |
| 0.8111 | 0.08 | 1000 | 0.7776 | 0.6659 |
| 0.7745 | 0.12 | 1500 | 0.7510 | 0.6748 |
| 0.7502 | 0.16 | 2000 | 0.7329 | 0.6886 |
| 0.7431 | 0.2 | 2500 | 0.7189 | 0.6921 |
| 0.7325 | 0.24 | 3000 | 0.7032 | 0.6991 |
| 0.7139 | 0.29 | 3500 | 0.6793 | 0.7129 |
| 0.7031 | 0.33 | 4000 | 0.6678 | 0.7215 |
| 0.6778 | 0.37 | 4500 | 0.6761 | 0.7236 |
| 0.6811 | 0.41 | 5000 | 0.6541 | 0.7253 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
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} | 5 | 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: chqmatteo/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-qa-mlqa | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: QRDQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 523.00 +/- 124.18
name: mean_reward
verified: false
---
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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 qrdqn --env SpaceInvadersNoFrameskip-v4 -orga ospeek -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga ospeek -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ospeek
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('normalize', False)])
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | {
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}
} | 27 | null | ---
license: creativeml-openrail-m
---
https://civitai.com/models/3745/anytwam-11-mixedmodel |
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 | 2023-01-14T11:20:25Z | ---
tags:
- Pong-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pong-v5
type: Pong-v5
metrics:
- type: mean_reward
value: 19.90 +/- 1.14
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Pong-v5**
This is a trained model of a PPO agent playing Pong-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_async_jax_scan_impalanet_machado.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[ppo_atari_envpool_async_jax_scan_impalanet_machado]"
python -m cleanrl_utils.enjoy --exp-name ppo_atari_envpool_async_jax_scan_impalanet_machado --env-id Pong-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Pong-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/ppo_atari_envpool_async_jax_scan_impalanet_machado.py
curl -OL https://huggingface.co/cleanrl/Pong-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Pong-v5-ppo_atari_envpool_async_jax_scan_impalanet_machado-seed1/raw/main/poetry.lock
poetry install --all-extras
python ppo_atari_envpool_async_jax_scan_impalanet_machado.py --track --wandb-project-name envpool-atari --save-model --upload-model --hf-entity cleanrl --env-id Pong-v5 --seed 1
```
# Hyperparameters
```python
{'anneal_lr': True,
'async_batch_size': 16,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Pong-v5',
'exp_name': 'ppo_atari_envpool_async_jax_scan_impalanet_machado',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1024,
'norm_adv': True,
'num_envs': 64,
'num_minibatches': 2,
'num_steps': 32,
'num_updates': 24414,
'save_model': True,
'seed': 1,
'target_kl': None,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 2,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'envpool-atari'}
```
|
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": {
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}
} | 1 | 2023-01-14T11:52:33Z | ---
language:
- en
pipeline_tag: token-classification
---
Named Entity Recognition (NER) model to recognize gene and protein entities.
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
- [miRNA-Test-Corpus](https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html): entity type "Genes/Proteins"
- [CellFinder](https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/): entity type "GeneProtein"
- [CoMAGC](http://biopathway.org/CoMAGC/): entity "Gene"
- [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "PR"
- [GREC Corpus](http://www.nactem.ac.uk/GREC/standoff.php): entity types "Gene", "Protein", "Protein_Complex", "Enzyme"
- [JNLPBA](http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004): entity types "protein", "DNA", "RNA"
- [PGxCorpus](https://www.nature.com/articles/s41597-019-0342-9): entity type "Gene_or_protein"
- [FSU_PRGE](https://julielab.de/Resources/FSU_PRGE.html): entity types "protein", "protein_complex", "protein_familiy_or_group"
- [BC2GM corpus](https://github.com/spyysalo/bc2gm-corpus)- [](): entity type
- [CHEMPROT](https://biocreative.bioinformatics.udel.edu/resources/corpora/chemprot-corpus-biocreative-vi/): entity types "GENE-Y", "GENE-N"
- [mTOR pathway event corpus](https://github.com/openbiocorpora/mtor-pathway/tree/master/original-data): entity type "Protein"
- [DNA Methylation](https://github.com/openbiocorpora/dna-methylation/tree/master/original-data)
- [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-ggp-IOB): entity type "Gene/protein"
- [BioNLP09](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP09-IOB)
- [BioNLP11EPI](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11EPI-IOB)
- [BioNLP13CG](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13CG-ggp-IOB): entity type "gene_or_gene_product"
- [BioNLP13GE](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13GE-IOB): entity type "Protein"
- [BioNLP13PC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13PC-ggp-IOB): entity type "Gene_or_gene_product"
- [MLEE](http://nactem.ac.uk/MLEE/): entity type "Gene_or_gene_product" |
CennetOguz/distilbert-base-uncased-finetuned-recipe | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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}
} | 2 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
A fine-tuned stable diffusion model trained on Hassan Fathy architectural style.
|
Chaddmckay/Cdm | []
| null | {
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} | 0 | null | ---
language:
- ja
- en
---
# yumekawa_diffusion
[](https://colab.research.google.com/drive/1jUvv2RXEFo-fQVSSzVBs7vU22au3OLcC?usp=sharing#scrollTo=Ri6jRc9kAust)
Generate a "Yumekawaii" image
Do not use too many Prompts.
Negative Prompt should be low quality, bad face, bad hands, etc.
<img src="https://s3.amazonaws.com/moonup/production/uploads/1673848269039-63056e2d99870e13d3df4e73.png" width="900" >
# How to run
You can run in Google colab[](https://colab.research.google.com/drive/1jUvv2RXEFo-fQVSSzVBs7vU22au3OLcC?usp=sharing#scrollTo=Ri6jRc9kAust)
Use [🤗HuggingFace Diffusers library](https://github.com/huggingface/diffusers)
clone the library
```
pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy
```
and run folowing scripts
```python
import torch
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
model_id = "teftef/yumekawa_diffusion_ver2.1"
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler)
pipe = pipe.to("cuda")
prompt = "a girl"
negative_prompt="low quality,bad face,bad anatomy,bad hand"
image = pipe(prompt,negative_prompt=negative_prompt).images[0]
image.save("output_img.png")
```
# Result
**Left** : Waifu Diffusion 1.4 e1
**Middle** : Fine tuned model without LoRA : **[can use in here]**(https://huggingface.co/teftef/teftef_fox_ear_2_0/tree/main)
**Right** : Fine tuning with RoLA (yumekawa_diffusion)
### Fixed Prompts
・Prompt : a girl,
・Negative Prompt : low quality,bad face,bad hands,
・Step : 28
・CFG Scale : 7.5
・Sampling method : Euler a
・ｗ×ｈ＝512×768
<img src="https://s3.amazonaws.com/moonup/production/uploads/1673772447629-63056e2d99870e13d3df4e73.png" width="700" >
### Various Prompts
・Negative Prompt : low quality,bad face,bad hands,
・Step : 28
・CFG Scale : 7.5
・Sampling method : Euler a
・ｗ×ｈ＝512×768
<img src="https://s3.amazonaws.com/moonup/production/uploads/1673771856440-63056e2d99870e13d3df4e73.png" width="700" >
Traning : 2023/01/13
Public : 2023/01/15 teftef |
Chae/botman | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
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} | 5 | 2023-01-14T12:17:32Z | ---
language:
- en
pipeline_tag: token-classification
---
Named Entity Recognition (NER) model to recognize organism entities.
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
- [CellFinder](http://cellfinder.org/about/annotation/): entity type "species"
- [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "NCBITaxon"
- [MLEE](http://nactem.ac.uk/MLEE/):entity type "organism"
- [LINNAEUS](http://linnaeus.sourceforge.net/) (train and dev sets):
- [Species-800](https://species.jensenlab.org/)
- [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-species-IOB): entity type "Organism"
- [BioNLP13CG](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13CG-species-IOB): entity types "Organism", "Organism subdivision"
- [miRNA-Test-Corpus](https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html): entity type "species"
- [Mantra](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986661/pdf/ocv037.pdf):entity type "DISO"
|
Chaewon/mmnt_decoder_en | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 12 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: lunared473/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Chaewon/mnmt_decoder_en_gpt2 | []
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} | 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Galosa/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
chainyo/speaker-recognition-meetup | []
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} | 1 | null | ---
language:
- en
pipeline_tag: token-classification
---
Named Entity Recognition (NER) model to recognize biological process entities (as defined by Gene Ontology-Biological Process sub-ontology).
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following dataset:
- [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "GO-BP"
|
Charlotte/text2dm_models | []
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} | 0 | null | ---
tags:
- conversational
---
# Kirito DialoGPT Model |
Cheatham/xlm-roberta-large-finetuned4 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | {
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"XLMRobertaForSequenceClassification"
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} | 20 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: small-mlm-glue-sst2-target-glue-qnli
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. -->
# small-mlm-glue-sst2-target-glue-qnli
This model is a fine-tuned version of [muhtasham/small-mlm-glue-sst2](https://huggingface.co/muhtasham/small-mlm-glue-sst2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3630
- Accuracy: 0.8495
## 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: 3e-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: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4855 | 0.15 | 500 | 0.3953 | 0.8254 |
| 0.4445 | 0.31 | 1000 | 0.3830 | 0.8325 |
| 0.4215 | 0.46 | 1500 | 0.3571 | 0.8422 |
| 0.4085 | 0.61 | 2000 | 0.3721 | 0.8384 |
| 0.4111 | 0.76 | 2500 | 0.3340 | 0.8556 |
| 0.3924 | 0.92 | 3000 | 0.3283 | 0.8556 |
| 0.3534 | 1.07 | 3500 | 0.3431 | 0.8550 |
| 0.3177 | 1.22 | 4000 | 0.3506 | 0.8567 |
| 0.3156 | 1.37 | 4500 | 0.3498 | 0.8547 |
| 0.3208 | 1.53 | 5000 | 0.3630 | 0.8495 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
ChrisP/xlm-roberta-base-finetuned-marc-en | []
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} | 0 | 2023-01-14T14:32:38Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- LewisShanghai/autotrain-data-books-rating-analysis
co2_eq_emissions:
emissions: 13.050690238461922
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 2885184365
- CO2 Emissions (in grams): 13.0507
## Validation Metrics
- Loss: 0.797
- Accuracy: 0.652
- Macro F1: 0.425
- Micro F1: 0.652
- Weighted F1: 0.637
- Macro Precision: 0.396
- Micro Precision: 0.652
- Weighted Precision: 0.634
- Macro Recall: 0.478
- Micro Recall: 0.652
- Weighted Recall: 0.652
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/LewisShanghai/autotrain-books-rating-analysis-2885184365
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("LewisShanghai/autotrain-books-rating-analysis-2885184365", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("LewisShanghai/autotrain-books-rating-analysis-2885184365", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
ChrisVCB/DialoGPT-medium-cmjs | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
],
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} | 7 | null | ---
language:
- en
pipeline_tag: token-classification
---
Named Entity Recognition (NER) model to recognize variant entities. Here variant entity is a DNA-level or protein-level mutation as defined by the [Human Genome Variation Society nomenclature](http://varnomen.hgvs.org/).
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets:
- [tmVar](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/): entity types "DNAMutation", "ProteinMutation", "SNP"
- [PGxCorpus](https://www.nature.com/articles/s41597-019-0342-9): entity type "Limited_variation"
- [SNPPhenA](https://figshare.com/s/f19191317056d6835b38): entity type "SNP" |
ChrisVCB/DialoGPT-medium-ej | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
],
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} | 13 | 2023-01-14T14:34:03Z | ---
language:
- hi
license: apache-2.0
tags:
- whisper-event
metrics:
- wer
model-index:
- name: Whisper Hindi Large-v2 - Vasista Sai Lodagala
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: hi_in
split: test
metrics:
- type: wer
value: 6.8
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
metrics:
- type: wer
value: 10.98
name: WER
---
<!-- 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 Hindi Large-v2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Hindi data available from multiple publicly available ASR corpuses.
It has been fine-tuned as a part of the Whisper fine-tuning sprint.
**NOTE:** The code used to train this model is available for re-use in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository.
## Usage
In order to evaluate this model on an entire dataset, the evaluation codes available in the [whisper-finetune](https://github.com/vasistalodagala/whisper-finetune) repository can be used.
The same repository also provides the scripts for faster inference using whisper-jax.
In order to infer a single audio file using this model, the following code snippet can be used:
```python
>>> import torch
>>> from transformers import pipeline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
>>> transcribe = pipeline(task="automatic-speech-recognition", model="vasista22/whisper-hindi-large-v2", chunk_length_s=30, device=device)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
For faster inference of whisper models, the [whisper-jax](https://github.com/sanchit-gandhi/whisper-jax) library can be used. Please follow the necessary installation steps as mentioned [here](https://github.com/vasistalodagala/whisper-finetune#faster-evaluation-with-whisper-jax), before using the following code snippet:
```python
>>> import jax.numpy as jnp
>>> from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
>>> # path to the audio file to be transcribed
>>> audio = "/path/to/audio.format"
>>> transcribe = FlaxWhisperPipline("vasista22/whisper-hindi-large-v2", batch_size=16)
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe")
>>> print('Transcription: ', transcribe(audio)["text"])
```
## Training and evaluation data
Training Data:
- [GramVaani ASR Corpus](https://sites.google.com/view/gramvaaniasrchallenge/dataset?authuser=0)
- [ULCA ASR Corpus](https://github.com/Open-Speech-EkStep/ULCA-asr-dataset-corpus#hindi-labelled--total-duration-is-239876-hours)
- [Shrutilipi ASR Corpus](https://ai4bharat.org/shrutilipi)
- [Google/Fleurs Train+Dev set](https://huggingface.co/datasets/google/fleurs)
Evaluation Data:
- [GramVaani ASR Corpus Test Set](https://sites.google.com/view/gramvaaniasrchallenge/dataset?authuser=0)
- [Google/Fleurs Test Set](https://huggingface.co/datasets/google/fleurs)
## Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.75e-05
- train_batch_size: 8
- eval_batch_size: 24
- seed: 22
- optimizer: adamw_bnb_8bit
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 25000
- training_steps: 57000 (Initially set to 116255 steps)
- mixed_precision_training: True
## Acknowledgement
This work was done at [Speech Lab, IIT Madras](https://asr.iitm.ac.in/).
The compute resources for this work were funded by "Bhashini: National Language translation Mission" project of the Ministry of Electronics and Information Technology (MeitY), Government of India. |
ChristianOrr/madnet_keras | [
"tensorboard",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"license:apache-2.0"
]
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} | 0 | 2023-01-14T14:36:31Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### xridl Dreambooth model trained by Suniljl 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:
|
Chungu424/repodata | []
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} | 0 | 2023-01-14T15:14:06Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/3240/aih-megamerge |
Chuu/Chumar | []
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} | 0 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Monte_Carlo_Policy_Gradient
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
|
CodeNinja1126/bert-p-encoder | [
"pytorch"
]
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} | 3 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Ghosty18/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CodeNinja1126/bert-q-encoder | [
"pytorch"
]
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} | 3 | 2023-01-14T16:07:06Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-v1
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="OubeidAllahjb/FrozenLake-v1", 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"])
```
|
Contrastive-Tension/BERT-Large-NLI-CT | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 15 | null | ---
tags:
- generated_from_trainer
datasets:
- custom_squad_v2
model-index:
- name: kobigbird-pure8-73451783
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. -->
# kobigbird-pure8-73451783
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the custom_squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 8
- distributed_type: tpu
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 42 | 5.2658 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
]
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} | 38 | null | ---
tags:
- conversational
---
#Rick DialoGPT model |
Craig/mGqFiPhu | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
]
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} | 0 | 2023-01-14T16:59:49Z | ---
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
|
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2 | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
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} | 1 | 2023-01-14T17:57:29Z | ---
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="iikjl/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"])
```
|
D3xter1922/distilbert-base-uncased-finetuned-cola | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9774436090225563
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9774
- Loss: 0.0876
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.26 | 1.0 | 130 | 0.9549 | 0.2285 |
| 0.277 | 2.0 | 260 | 0.9925 | 0.1066 |
| 0.1629 | 3.0 | 390 | 0.9699 | 0.1069 |
| 0.0963 | 4.0 | 520 | 0.9774 | 0.0885 |
| 0.1569 | 5.0 | 650 | 0.9774 | 0.0876 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DCU-NLP/electra-base-irish-cased-discriminator-v1 | [
"pytorch",
"electra",
"pretraining",
"ga",
"transformers",
"irish",
"license:apache-2.0"
]
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} | 4 | null | ---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- science
widget:
- text: a photo of space nebula in the Acropolis
---
# DreamBooth model for the space concept trained by aaparajit02 on the aaparajit02/nebula dataset.
This is a Stable Diffusion model fine-tuned on the space concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of space nebula**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `nebula` images for the science theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('aaparajit02/space-nebula')
image = pipeline().images[0]
image
```
|
DHBaek/gpt2-stackoverflow-question-contents-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 14 | null | Access to model taner11oner/newage is restricted and you are not in the authorized list. Visit https://huggingface.co/taner11oner/newage to ask for access. |
DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
]
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} | 14 | 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
|
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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"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,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 27 | 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: 267.14 +/- 14.49
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
...
```
|
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