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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-16 06:27:54
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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listlengths 1
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| pipeline_tag
stringclasses 55
values | createdAt
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|---|---|---|---|---|---|---|---|---|---|
dhanyaXchandra/deariebdsm | dhanyaXchandra | 2023-03-22T06:43:27Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-22T06:38:46Z | ---
license: creativeml-openrail-m
---
|
dhanyaXchandra/povmissionary | dhanyaXchandra | 2023-03-22T06:38:06Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-22T06:35:04Z | ---
license: creativeml-openrail-m
---
|
dhanyaXchandra/inniesbettervulva | dhanyaXchandra | 2023-03-22T06:34:30Z | 0 | 2 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-22T06:15:55Z | ---
license: creativeml-openrail-m
---
|
EricLiang98/MigBERT-large | EricLiang98 | 2023-03-22T06:30:52Z | 8 | 1 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"Roberta",
"Chinese Pre-trained Language Model",
"zh",
"arxiv:2303.10893",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-03-20T05:59:28Z | ---
license: apache-2.0
language:
- zh
library_name: transformers
tags:
- Roberta
- Chinese Pre-trained Language Model
---
Please use 'XLMRoberta' related functions to load this model!
# MigBERT | 中文混合粒度预训练模型
[Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models](https://arxiv.org/abs/2303.10893)
# Demo | 使用样例
https://github.com/xnliang98/MigBERT
# Citation
如果你觉得我们的工作对你有用，请在您的工作中引用我们的文章。
If you find our resource or paper is useful, please consider including the following citation in your paper.
```
@misc{liang2023character,
title={Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models},
author={Xinnian Liang and Zefan Zhou and Hui Huang and Shuangzhi Wu and Tong Xiao and Muyun Yang and Zhoujun Li and Chao Bian},
year={2023},
eprint={2303.10893},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
badmatr11x/distilroberta-base-offensive-hateful-speech-text-multiclassification | badmatr11x | 2023-03-22T06:24:41Z | 29,012 | 7 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"code",
"en",
"dataset:badmatr11x/hate-offensive-speech",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-16T20:52:22Z | ---
license: mit
datasets:
- badmatr11x/hate-offensive-speech
language:
- en
pipeline_tag: text-classification
tags:
- code
---
This is pre-trained model for offensive and hateful speech multiclassification.
Model is fine-tuned on distilroberta-base model from the huggingface repository.
This model is pre-trained on my original datasets. Reported accuracy of this model is **94.50%**.
You can find all the datasets on this [repository](https://huggingface.co/datasets/badmatr11x/hate-offensive-speech).
Find out my Space on this model [here](https://huggingface.co/spaces/badmatr11x/offensive-hateful-speech-multiclassification).
You can find training notebook on my github profile [@purveshpatel511](https://github.com/purveshpatel511/offensive-hateful-text-multiclassification/blob/master/text-multiclassification.ipynb).
Report any bugs or issue [here](https://github.com/purveshpatel511/offensive-hateful-text-multiclassification/issues).
Checkout my GitHub Profile [@purveshpatel511](https://github.com/purveshpatel511). |
shuxue051/pokemon-lora | shuxue051 | 2023-03-22T06:22:46Z | 4 | 1 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-03-21T05:40:23Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/shuxue051/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
dhanyaXchandra/mleggesture | dhanyaXchandra | 2023-03-22T06:15:24Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-22T06:02:27Z | ---
license: creativeml-openrail-m
---
|
pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed232 | pfunk | 2023-03-22T06:04:17Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T06:04:08Z | ---
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: -20.97 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **PongNoFrameskip-v4**
This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x2.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[P_DQPN_x2]"
python -m cleanrl_utils.enjoy --exp-name P_DQPN_x2 --env-id PongNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed232/raw/main/dqpn_freq_atari.py
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed232/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed232/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x2 --policy-network-frequency 2000 --seed 232
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq_atari.py',
'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'double_learning': False,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'P_DQPN_x2',
'exploration_fraction': 0.2,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 10000,
'max_gradient_norm': inf,
'policy_network_frequency': 2000,
'policy_tau': 1.0,
'save_model': True,
'seed': 232,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 5000000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed888 | pfunk | 2023-03-22T05:58:32Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T05:58:23Z | ---
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQPN_freq
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: 20.30 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQPN_freq** Agent Playing **PongNoFrameskip-v4**
This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x2.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[P_DQPN_x2]"
python -m cleanrl_utils.enjoy --exp-name P_DQPN_x2 --env-id PongNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed888/raw/main/dqpn_freq_atari.py
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed888/raw/main/poetry.lock
poetry install --all-extras
python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x2 --policy-network-frequency 2000 --seed 888
```
# Hyperparameters
```python
{'alg_type': 'dqpn_freq_atari.py',
'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'double_learning': False,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'P_DQPN_x2',
'exploration_fraction': 0.2,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 10000,
'max_gradient_norm': inf,
'policy_network_frequency': 2000,
'policy_tau': 1.0,
'save_model': True,
'seed': 888,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 5000000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
ElementBrawlerAI/PyramidsRND | ElementBrawlerAI | 2023-03-22T05:57:07Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-22T05:56:43Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: ElementBrawlerAI/PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dhanyaXchandra/pyoapple | dhanyaXchandra | 2023-03-22T05:52:00Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-03-22T05:50:38Z | ---
license: creativeml-openrail-m
---
|
ElementBrawlerAI/SnowballTarget | ElementBrawlerAI | 2023-03-22T05:47:18Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-22T05:47:11Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **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: Find your model_id: ElementBrawlerAI/SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
yukinagato/ppo-LunarLander-v2 | yukinagato | 2023-03-22T05:36:50Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T05:36:26Z | ---
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: 258.44 +/- 20.99
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
...
```
|
aidenlee/q-FrozenLake-v1-4x4-noSlippery | aidenlee | 2023-03-22T05:06:36Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T05:06:33Z | ---
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="aidenlee/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"])
```
|
PavelDanek/s2g_class_cours | PavelDanek | 2023-03-22T05:02:39Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain",
"unk",
"dataset:PavelDanek/autotrain-data-s2g_text_class",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-22T05:01:25Z | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- PavelDanek/autotrain-data-s2g_text_class
co2_eq_emissions:
emissions: 0.4838643866239026
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 42689109103
- CO2 Emissions (in grams): 0.4839
## Validation Metrics
- Loss: 0.392
- Accuracy: 0.845
- Precision: 0.800
- Recall: 0.787
- AUC: 0.898
- F1: 0.793
## 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/PavelDanek/autotrain-s2g_text_class-42689109103
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("PavelDanek/autotrain-s2g_text_class-42689109103", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("PavelDanek/autotrain-s2g_text_class-42689109103", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
jakub014/ColD-Fusion-finetuned-convincingness-IBM | jakub014 | 2023-03-22T05:01:55Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-22T04:44:09Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ColD-Fusion-finetuned-convincingness-IBM
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. -->
# ColD-Fusion-finetuned-convincingness-IBM
This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9911
- Accuracy: 0.7743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 270 | 0.5708 | 0.7482 |
| 0.4293 | 2.0 | 540 | 0.5972 | 0.7721 |
| 0.4293 | 3.0 | 810 | 0.7097 | 0.7634 |
| 0.2106 | 4.0 | 1080 | 0.8812 | 0.7656 |
| 0.2106 | 5.0 | 1350 | 0.9911 | 0.7743 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
golightly/a2c-PandaReachDense-v2 | golightly | 2023-03-22T04:48:53Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T04:46:17Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.39 +/- 1.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hans0812/densenet-ai-generated-classifier | hans0812 | 2023-03-22T04:47:25Z | 0 | 0 | null | [
"en",
"dataset:competitions/aiornot",
"license:mit",
"region:us"
]
| null | 2023-03-22T04:43:40Z | ---
license: mit
datasets:
- competitions/aiornot
language:
- en
--- |
jakub014/ColD-Fusion-finetuned-convincingness-acl2016 | jakub014 | 2023-03-22T04:39:11Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-22T03:48:20Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ColD-Fusion-finetuned-convincingness-acl2016
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. -->
# ColD-Fusion-finetuned-convincingness-acl2016
This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4112
- Accuracy: 0.9275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4318 | 1.0 | 583 | 0.2267 | 0.9047 |
| 0.2063 | 2.0 | 1166 | 0.1945 | 0.9142 |
| 0.1647 | 3.0 | 1749 | 0.3107 | 0.9155 |
| 0.1179 | 4.0 | 2332 | 0.3730 | 0.9215 |
| 0.0669 | 5.0 | 2915 | 0.4112 | 0.9275 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Jimmie/urban8k | Jimmie | 2023-03-22T04:13:08Z | 0 | 0 | fastai | [
"fastai",
"region:us"
]
| null | 2023-03-22T01:58:38Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
figuringoutmine/translator-for-travel-jp-to-kr | figuringoutmine | 2023-03-22T04:12:32Z | 3 | 3 | transformers | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"python",
"transformer",
"translation",
"ja",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-03-16T11:27:21Z | ---
pipeline_tag: translation
language:
- ja
- ko
tags:
- python
- transformer
- pytorch
---
https://github.com/akpe12/JP-KR-ocr-translator-for-travel
- Usage
```
from transformers import(
EncoderDecoderModel,
PreTrainedTokenizerFast,
# XLMRobertaTokenizerFast,
BertTokenizerFast,
)
encoder_model_name = "cl-tohoku/bert-base-japanese-v2"
decoder_model_name = "skt/kogpt2-base-v2"
src_tokenizer = BertTokenizerFast.from_pretrained(encoder_model_name)
trg_tokenizer = PreTrainedTokenizerFast.from_pretrained(decoder_model_name)
model = EncoderDecoderModel.from_pretrained("figuringoutmine/translator-for-travel-jp-to-kr")
```
```
text = "豚骨ラーメン"
embeddings = src_tokenizer(text, return_attention_mask=False, return_token_type_ids=False, return_tensors='pt')
embeddings = {k: v for k, v in embeddings.items()}
output = model.generate(**embeddings)[0, 1:-1]
trg_tokenizer.decode(output.cpu())
```
- Quantitative evaluation using data related traveling in Japan
<br>
with BLEU score(1-gram)
<br>
Papago: 51.9
<br>
Google: 32.8
<br>
<strong>figuringoutmine/translator-for-travel-jp-to-kr: 52.7<strong/>
|
r1ck/vi-sentence-embedding | r1ck | 2023-03-22T04:03:46Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"sentence-similarity",
"vi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-03-22T03:59:15Z | ---
license: apache-2.0
language:
- vi
pipeline_tag: sentence-similarity
--- |
golightly/a2c-AntBulletEnv-v0 | golightly | 2023-03-22T03:53:02Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T03:51:43Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 2194.65 +/- 81.67
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
FelipePasquevich/dqn-SpaceInvadersNoFrameskip-v4 | FelipePasquevich | 2023-03-22T03:51:01Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T03:50:20Z | ---
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: 592.00 +/- 146.92
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga FelipePasquevich -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 FelipePasquevich -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 FelipePasquevich
```
## 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)])
```
|
nobody-here/chatglm-6b-int4 | nobody-here | 2023-03-22T03:32:45Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"chatglm",
"glm",
"thudm",
"custom_code",
"zh",
"en",
"endpoints_compatible",
"region:us"
]
| null | 2023-03-22T00:26:16Z | ---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM-6B
## 介绍
ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 [ChatGLM](https://chatglm.cn) 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。
ChatGLM-6B-INT4 是 ChatGLM-6B 量化后的模型权重。具体的，ChatGLM-6B-INT4 对 ChatGLM-6B 中的 28 个 GLM Block 进行了 INT4 量化，没有对 Embedding 和 LM Head 进行量化。量化后的模型理论上 6G 显存（使用 CPU 即内存）即可推理，具有在嵌入式设备（如树莓派）上运行的可能。
在 CPU 上运行时，会根据硬件自动编译 CPU Kernel ，请确保已安装 GCC 和 OpenMP （Linux一般已安装，对于Windows则需手动安装），以获得最佳并行计算能力。
## 软件依赖
```shell
pip install protobuf==3.20.0 transformers==4.26.1 icetk cpm_kernels
```
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).half().cuda()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM-6B)。
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文：
```
@inproceedings{
zeng2023glm-130b,
title={{GLM}-130B: An Open Bilingual Pre-trained Model},
author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=-Aw0rrrPUF}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
``` |
redstonehero/Yiffymix | redstonehero | 2023-03-22T03:30:27Z | 28 | 7 | diffusers | [
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-03-21T21:47:41Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
--- |
haril/baby | haril | 2023-03-22T03:03:40Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"chemistry",
"zh",
"dataset:fka/awesome-chatgpt-prompts",
"license:openrail",
"region:us"
]
| null | 2023-03-22T03:01:46Z | ---
license: openrail
datasets:
- fka/awesome-chatgpt-prompts
language:
- zh
metrics:
- accuracy
library_name: adapter-transformers
tags:
- chemistry
--- |
yonathanstwn/opus-mt-id-en-ccmatrix-v2 | yonathanstwn | 2023-03-22T03:03:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:ccmatrix",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-20T18:22:04Z | ---
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: opus-mt-id-en-ccmatrix-v2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-id
split: train
args: en-id
metrics:
- name: Bleu
type: bleu
value: 52.173
---
<!-- 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. -->
# opus-mt-id-en-ccmatrix-v2
This model was trained from scratch on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7667
- Bleu: 52.173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:------:|:---------------:|:-------:|
| 0.803 | 1.0 | 148438 | 0.8185 | 50.3216 |
| 0.7212 | 2.0 | 296876 | 0.7904 | 51.282 |
| 0.6913 | 3.0 | 445314 | 0.7791 | 51.7806 |
| 0.6727 | 4.0 | 593752 | 0.7691 | 52.0263 |
| 0.6609 | 5.0 | 742190 | 0.7667 | 52.173 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.11.0
|
MnLgt/muir-inversion | MnLgt | 2023-03-22T03:01:01Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2023-03-22T03:00:58Z | ---
license: mit
---
### muir-inversion on Stable Diffusion
This is the `<muir-chair>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
ThePianist/dqn-SpaceInvadersNoFrameskip-v4 | ThePianist | 2023-03-22T02:50:00Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T02:49:18Z | ---
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: 535.00 +/- 106.40
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ThePianist -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 ThePianist -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 ThePianist
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 2000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
YoungBeauty/xlm-roberta-base-finetuned-panx-all | YoungBeauty | 2023-03-22T02:48:00Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-03-21T22:50:43Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1737
- F1: 0.8533
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3038 | 1.0 | 835 | 0.2007 | 0.7976 |
| 0.1548 | 2.0 | 1670 | 0.1850 | 0.8361 |
| 0.1013 | 3.0 | 2505 | 0.1737 | 0.8533 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DJDonovan/ppo-pyramids | DJDonovan | 2023-03-22T02:47:05Z | 8 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-22T02:42:15Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: DJDonovan/ppo-pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
siyuanzhu/Roberta-Large-disinfo | siyuanzhu | 2023-03-22T02:46:18Z | 0 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-03-21T18:52:07Z | ---
license: cc-by-nc-sa-4.0
---
|
huggingtweets/godlessbot | huggingtweets | 2023-03-22T02:34:29Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-22T02:34:18Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1531763535361069056/Rdqf48Bx_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">excerpts</div>
<div style="text-align: center; font-size: 14px;">@godlessbot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from excerpts.
| Data | excerpts |
| --- | --- |
| Tweets downloaded | 3215 |
| Retweets | 2 |
| Short tweets | 6 |
| Tweets kept | 3207 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8s5ml4lo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @godlessbot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/etg4pjhq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/etg4pjhq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/godlessbot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
helpingstar/ppo-SnowballTarget | helpingstar | 2023-03-22T02:32:38Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-22T02:32:33Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **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: Find your model_id: helpingstar/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
lotek93/poca-SoccerTwos | lotek93 | 2023-03-22T02:28:07Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-03-22T02:28:01Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: lotek93/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
yonathanstwn/opus-mt-en-id-ccmatrix-v2 | yonathanstwn | 2023-03-22T02:24:47Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:ccmatrix",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-20T18:22:07Z | ---
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: opus-mt-en-id-ccmatrix-v2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: ccmatrix
type: ccmatrix
config: en-id
split: train
args: en-id
metrics:
- name: Bleu
type: bleu
value: 61.047
---
<!-- 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. -->
# opus-mt-en-id-ccmatrix-v2
This model was trained from scratch on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6976
- Bleu: 61.047
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:------:|:---------------:|:-------:|
| 0.7247 | 1.0 | 148438 | 0.7471 | 59.75 |
| 0.6448 | 2.0 | 296876 | 0.7208 | 60.3385 |
| 0.6157 | 3.0 | 445314 | 0.7081 | 60.7261 |
| 0.5982 | 4.0 | 593752 | 0.7009 | 60.9462 |
| 0.5872 | 5.0 | 742190 | 0.6976 | 61.047 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.11.0
|
voidful/bart-distractor-generation | voidful | 2023-03-22T02:11:44Z | 20 | 4 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"distractor",
"generation",
"seq2seq",
"en",
"dataset:race",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-03-02T23:29:05Z | ---
language: en
tags:
- bart
- distractor
- generation
- seq2seq
datasets:
- race
metrics:
- bleu
- rouge
pipeline_tag: text2text-generation
widget:
- text: "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another special choice : treehouses . In France , treehouses are offered to travelers as a new choice in many places . The price may be a little higher , but you do have a chance to _ your childhood memories . Alain Laurens , one of France ' s top treehouse designers , said , ' Most of the people might have the experience of building a den when they were young . And they like that feeling of freedom when they are children . ' Its fairy - tale style gives travelers a special feeling . It seems as if they are living as a forest king and enjoying the fresh air in the morning . Another kind of treehouse is the ' star cube ' . It gives travelers the chance of looking at the stars shining in the sky when they are going to sleep . Each ' star cube ' not only offers all the comfortable things that a hotel provides for travelers , but also gives them a chance to look for stars by using a telescope . The glass roof allows you to look at the stars from your bed . </s> The passage mainly tells us </s> treehouses in france."
---
# bart-distractor-generation
## Model description
This model is a sequence-to-sequence distractor generator which takes an answer, question and context as an input, and generates a distractor as an output. It is based on a pretrained `bart-base` model.
For details, please see https://github.com/voidful/BDG.
## Intended uses & limitations
The model is trained to generate examinations-style multiple choice distractor. The model performs best with full sentence answers.
#### How to use
The model takes concatenated context, question and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format:
```
context </s> question </s> answer
```
The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.
For details, please see https://github.com/voidful/BDG.
#### Limitations and bias
The model is limited to generating distractor in the same style as those found in [RACE](https://www.aclweb.org/anthology/D17-1082/). The generated distractors can potentially be leading or reflect biases that are present in the context. If the context is too short or completely absent, or if the context, question and answer do not match, the generated distractor is likely to be incoherent. |
DemonPan/ppo-Huggy | DemonPan | 2023-03-22T01:52:57Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-03-22T01:52:51Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **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: Find your model_id: DemonPan/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DJDonovan/ppo-SnowballTarget | DJDonovan | 2023-03-22T01:52:08Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-22T01:52:02Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **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: Find your model_id: DJDonovan/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
thu-coai/LongLM-small | thu-coai | 2023-03-22T01:49:03Z | 9 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"lm-head",
"zh",
"arxiv:2108.12960",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-03-02T23:29:05Z | ---
language:
- zh
thumbnail: http://coai.cs.tsinghua.edu.cn/coai/img/logo.png?v=13923
tags:
- pytorch
- lm-head
- zh
metrics:
widget:
- text: "小咕噜对靳司寒完全是个自来熟，小家伙爬进他怀里小手搂着他的脖子，奶声奶气的要求：“靳蜀黎,你给咕噜讲故事好不好？”讲故事？童话故事吗？“我不会。”小家伙明显不信。嘟着小嘴大眼汪汪的盯着他，“哼。”小家伙轻轻哼了一声,靳司寒默了半晌，<extra_id_1>"
- text: "美女亲自打招呼，这可是破天荒第一次，之前不管他献多少次殷勤，美女<extra_id_1>甩他，难道今天真是老天<extra_id_2>不敢<extra_id_3>的兄连滚带爬的来到<extra_id_4>身边队友都带着艳<extra_id_5>他，<extra_id_6>连计算机系的那票球友都在那儿不住地偷看MAGGIE，这种感觉真<extra_id_7>毙了！"
inference:
parameters:
top_p: 0.9
---
## LongLM
### 1. Parameters
| Versions | $d_m$ | $d_{ff}$ | $d_{kv}$ | $n_h$ | $n_e/n_d$ | \# P |
| ------------ | ----- | -------- | -------- | ----- | --------- | ---- |
| LongLM-small | 512 | 2,048 | 64 | 8 | 6/6 | 60M |
| LongLM-base | 768 | 3,072 | 64 | 12 | 12/12 | 223M |
| LongLM-large | 1,536 | 3,072 | 64 | 12 | 24/32 | 1B |
- $d_m$: the dimension of hidden states
- $d_{ff}$: the dimension of feed forward layers
- $d_{kv}$: the dimension of the keys/values in the self-attention layers
- $n_h$: the number of attention heads
- $n_e$: the number of hidden layers of the encoder
- $n_d$: the number of hidden layers of the decoder
- \#P: the number of parameters
### 2. Pretraining Tasks
Encoder-decoder models are trained typically by maximizing the likelihood of the target output given an input. To improve the capacities of both the encoder and decoder, we propose to train LongLM with two pretraining tasks including text infilling (Raffel et al., 2020) and conditional continuation (Radford et al., 2019). For the first task, the input is a text where a number of spans are sampled and replaced by special tokens with unique IDs, while the output is the spans delimited by the special tokens used in the input. The lengths of masked spans are drawn from a Poisson distribution with λ=3 and all masked tokens compress 15% of the original texts. As for the second task, the input and output are respectively the front and back half of a text, which is split into two parts randomly.
### 3. Pretraining Data
We collect 120G novels as the pretraining data for LongLM.
### 4. Checkpoints
1. **Model Loading:**
```python\
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained('LongLM-large')
tokenizer.add_special_tokens({"additional_special_tokens": ["<extra_id_%d>"%d for d in range(100)]})
model = T5ForConditionalGeneration.from_pretrained('LongLM-large')
```
2. **Generation:**
```python
input_ids = tokenizer("小咕噜对，<extra_id_1>",return_tensors="pt", padding=True, truncation=True, max_length=512).input_ids.to(device)
gen = model.generate(input_ids, do_sample=True, decoder_start_token_id=1, top_p=0.9, max_length=512)
```
### 5. Dependencies
```
datasets 1.6.2
deepspeed 0.3.16
huggingface-hub 0.0.8
jieba 0.42.1
jsonlines 2.0.0
nltk 3.5
numpy 1.19.5
pytorch-lightning 1.2.0
regex 2020.11.13
rouge 1.0.1
rouge-score 0.0.4
sacrebleu 1.5.0
scipy 1.5.4
sentencepiece 0.1.95
tokenizers 0.10.1
torch 1.8.1
torchaudio 0.8.0
torchmetrics 0.2.0
torchvision 0.9.0
transformers 4.6.1
```
### 6. Contributers
[Jian Guan](https://jianguanthu.github.io/) at [thu-coai](http://coai.cs.tsinghua.edu.cn/)
## Citation
```txt
@misc{guan2021lot,
title={LOT: A Benchmark for Evaluating Chinese Long Text Understanding and Generation},
author={Jian Guan and Zhuoer Feng and Yamei Chen and Ruilin He and Xiaoxi Mao and Changjie Fan and Minlie Huang},
year={2021},
eprint={2108.12960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
ElementBrawlerAI/Reinforce-CartPole-v1 | ElementBrawlerAI | 2023-03-22T01:32:30Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T01:32:16Z | ---
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
|
tuhailong/cross-encoder-bert-base | tuhailong | 2023-03-22T01:28:37Z | 16 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sbert",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-03-02T23:29:05Z | ---
language: zh
tags:
- sbert
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 20w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder
### Usage
```python
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEncoder('tuhailong/cross-encoder')
>>> scores = model.predict([["今天天气不错", "今天心情不错"]])
>>> print(scores)
``` |
ShreyasM/ppo-HopperBulletEnv-v0 | ShreyasM | 2023-03-22T01:27:54Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"HopperBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T01:13:16Z | ---
library_name: stable-baselines3
tags:
- HopperBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HopperBulletEnv-v0
type: HopperBulletEnv-v0
metrics:
- type: mean_reward
value: 70.43 +/- 3.91
name: mean_reward
verified: false
---
# **PPO** Agent playing **HopperBulletEnv-v0**
This is a trained model of a **PPO** agent playing **HopperBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cross-hedgehog/r62 | cross-hedgehog | 2023-03-22T01:25:36Z | 0 | 0 | keras | [
"keras",
"chat",
"license:mit",
"region:us"
]
| null | 2023-03-22T01:06:49Z | ---
license: mit
metrics:
- character
library_name: keras
tags:
- chat
---
# training
## minimum
* python3
* tensorflow
* numpy
* pandas
* at least 1gb of ram
* a lot of patience
# commands
python main.py
# dataset:
## origin
* based on 'danganronpa: trigger happy havoc'
* chatgpt
# scope
a simple-ish ai model for cahtting with Chihiro Fujisaki
i have no idea how to expand the initial_data.csv(has a couple of things, but it is nowhere near perfect), so i might not update this, or i will, its up to fate at the moment
# ai learning
the ai periodicaly relearns things based on your conversations and its original training data(as i mentioned, it lacks things)
# limitations
the ai is not perfect(no one is), just like a human, it will generate wrong information, but be patient, it is learning from its mistakes and it will eventualy be better then post production |
ldaquan1996/ppo-Pyramids | ldaquan1996 | 2023-03-22T01:15:10Z | 18 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-22T00:34:24Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: ldaquan1996/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
McCheng/a2c-AntBulletEnv-v0 | McCheng | 2023-03-22T00:55:01Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-22T00:54:10Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 2164.52 +/- 181.61
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
lamaabdulaziz/MarBERT-finetuned-fnd | lamaabdulaziz | 2023-03-22T00:48:01Z | 21 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-02-22T17:46:13Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: MarBERT-finetuned-fnd
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. -->
# MarBERT-finetuned-fnd
This model is a fine-tuned version of [UBC-NLP/MARBERT](https://huggingface.co/UBC-NLP/MARBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5055
- Macro F1: 0.7456
- Accuracy: 0.7624
- Precision: 0.7607
- Recall: 0.7402
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 25
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| 0.5335 | 1.0 | 1419 | 0.4917 | 0.7417 | 0.7504 | 0.7418 | 0.7416 |
| 0.3827 | 2.0 | 2838 | 0.5055 | 0.7456 | 0.7624 | 0.7607 | 0.7402 |
| 0.2637 | 3.0 | 4257 | 0.6907 | 0.7454 | 0.7579 | 0.7511 | 0.7422 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.10.3
|
Alex48/poca-SoccerTwos-v14 | Alex48 | 2023-03-22T00:40:54Z | 9 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-03-22T00:39:50Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: Alex48/poca-SoccerTwos-v14
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ldaquan1996/ppo-SnowballTarget | ldaquan1996 | 2023-03-22T00:24:20Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-03-22T00:24:13Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **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: Find your model_id: ldaquan1996/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
wjudy/text-summarization | wjudy | 2023-03-22T00:18:46Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-19T23:15:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: text-summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1306
---
<!-- 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. -->
# text-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5397
- Rouge1: 0.1306
- Rouge2: 0.0442
- Rougel: 0.1096
- Rougelsum: 0.1093
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8278 | 0.1276 | 0.0368 | 0.1083 | 0.1078 | 19.0 |
| No log | 2.0 | 124 | 2.6203 | 0.1333 | 0.0459 | 0.1127 | 0.1125 | 19.0 |
| No log | 3.0 | 186 | 2.5566 | 0.1292 | 0.0434 | 0.1075 | 0.1074 | 19.0 |
| No log | 4.0 | 248 | 2.5397 | 0.1306 | 0.0442 | 0.1096 | 0.1093 | 19.0 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
sdesai/narrativa-finetuned-wmt22-en-pt-br | sdesai | 2023-03-22T00:16:37Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-22T00:07:06Z | ---
tags:
- generated_from_trainer
model-index:
- name: narrativa-finetuned-wmt22-en-pt-br
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. -->
# narrativa-finetuned-wmt22-en-pt-br
This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-en-pt-translation](https://huggingface.co/Narrativa/mbart-large-50-finetuned-opus-en-pt-translation) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
- epoch = 3.0
- eval_bleu = 66.8543
- eval_gen_len = 13.5105
- eval_loss = 0.4934
- eval_runtime = 0:00:20.01
- eval_samples = 190
- eval_samples_per_second = 9.492
- eval_steps_per_second = 2.398
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
alabnii/jmedroberta-base-sentencepiece | alabnii | 2023-03-21T23:57:37Z | 711 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"roberta",
"medical",
"ja",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-11-11T02:46:57Z | ---
language: ja
license: cc-by-nc-sa-4.0
tags:
- roberta
- medical
mask_token: "[MASK]"
widget:
- text: "この患者は[MASK]と診断された。"
---
# alabnii/jmedroberta-base-sentencepiece
## Model description
This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).
This model is released under the [Creative Commons 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed) (CC BY-NC-SA 4.0).
#### Reference
Ja:
```
@InProceedings{sugimoto_nlp2023_jmedroberta,
author = "杉本海人 and 壹岐太一 and 知田悠生 and 金沢輝一 and 相澤彰子",
title = "J{M}ed{R}o{BERT}a: 日本語の医学論文にもとづいた事前学習済み言語モデルの構築と評価",
booktitle = "言語処理学会第29回年次大会",
year = "2023",
url = "https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/P3-1.pdf"
}
```
En:
```
@InProceedings{sugimoto_nlp2023_jmedroberta,
author = "Sugimoto, Kaito and Iki, Taichi and Chida, Yuki and Kanazawa, Teruhito and Aizawa, Akiko",
title = "J{M}ed{R}o{BERT}a: a Japanese Pre-trained Language Model on Academic Articles in Medical Sciences (in Japanese)",
booktitle = "Proceedings of the 29th Annual Meeting of the Association for Natural Language Processing",
year = "2023",
url = "https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/P3-1.pdf"
}
```
## Datasets used for pre-training
- abstracts (train: 1.6GB (10M sentences), validation: 0.2GB (1.3M sentences))
- abstracts & body texts (train: 0.2GB (1.4M sentences))
## How to use
**Input text must be converted to full-width characters（全角）in advance.**
You can use this model for masked language modeling as follows:
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-sentencepiece")
model.eval()
tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-sentencepiece")
texts = ['この患者は[MASK]と診断された。']
inputs = tokenizer.batch_encode_plus(texts, return_tensors='pt')
outputs = model(**inputs)
tokenizer.convert_ids_to_tokens(outputs.logits[0][1:-1].argmax(axis=-1))
# ['▁この', '患者は', 'ＡＭＬ', '▁', 'と診断された', '。']
```
Alternatively, you can employ [Fill-mask pipeline](https://huggingface.co/tasks/fill-mask).
```python
from transformers import pipeline
fill = pipeline("fill-mask", model="alabnii/jmedroberta-base-sentencepiece", top_k=10)
fill("この患者は[MASK]と診断された。")
#[{'score': 0.04239409416913986,
# 'token': 7698,
# 'token_str': 'ＡＭＬ',
# 'sequence': 'この患者はＡＭＬ と診断された。'},
# {'score': 0.03562006726861,
# 'token': 3298,
# 'token_str': 'ＳＬＥ',
# 'sequence': 'この患者はＳＬＥ と診断された。'},
# {'score': 0.025064188987016678,
# 'token': 10303,
# 'token_str': 'ＭＤＳ',
# 'sequence': 'この患者はＭＤＳ と診断された。'},
# ...
```
You can fine-tune this model on downstream tasks.
**See also sample Colab notebooks:** https://colab.research.google.com/drive/1BUD3DKOUMqcwIO3X5bYUOsR_wDzgOJcd?usp=sharing
## Tokenization
Each sentence is tokenized into tokens by [SentencePiece (Unigram)](https://huggingface.co/course/chapter6/7).
## Vocabulary
The vocabulary consists of 30000 tokens induced by [SentencePiece (Unigram)](https://huggingface.co/course/chapter6/7).
## Training procedure
The following hyperparameters were used during pre-training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20000
- training_steps: 2000000
- mixed_precision_training: Native AMP
## Note: Why do we call our model RoBERTa, not BERT?
As the config file suggests, our model is based on HuggingFace's `BertForMaskedLM` class. However, we consider our model as **RoBERTa** for the following reasons:
- We kept training only with max sequence length (= 512) tokens.
- We removed the next sentence prediction (NSP) training objective.
- We introduced dynamic masking (changing the masking pattern in each training iteration).
## Acknowledgements
This work was supported by Japan Japan Science and Technology Agency (JST) AIP Trilateral AI Research (Grant Number: JPMJCR20G9), and Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) (Project ID: jh221004), in Japan.
In this research work, we used the "[mdx: a platform for the data-driven future](https://mdx.jp/)". |
meghabagri/TestModelUpload | meghabagri | 2023-03-21T23:25:16Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-03-21T21:52:10Z | Text classification using Naive Bayes Machine Learning Model
---
metrics:
- accuracy
pipeline_tag:
- text-classification
--- |
kenasuka/afik | kenasuka | 2023-03-21T23:25:14Z | 12 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-03-21T23:20:52Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### afik Dreambooth model trained by kenasuka 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:
|
Deysi/sentiment_analysis_imbd_final | Deysi | 2023-03-21T22:57:41Z | 4 | 0 | keras | [
"keras",
"pytorch",
"tensorboard",
"albert",
"sentiment analysis",
"en",
"dataset:Deysi/split-imdb",
"region:us"
]
| null | 2023-03-21T21:06:04Z | ---
datasets:
- Deysi/split-imdb
language:
- en
metrics:
- f1
library_name: keras
tags:
- sentiment analysis
--- |
n6ai/graphic-art | n6ai | 2023-03-21T22:31:22Z | 35 | 16 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"en",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-02-23T15:39:38Z | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
tags:
- text-to-image
- stable-diffusion
base_model: stabilityai/stable-diffusion-2-1
---
# graphic-art
Custom Stable Diffusion checkpoint (based on [SD 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1)) for generating graphic-design related images.
> Original model by [pmejna](https://civitai.com/user/pmejna) on [CivitAI](https://civitai.com/models/7884/graphic-art).
> ⚠️ For best results set at least one side to `768px`.
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model with Hugging Face pipelines,
[read the docs](https://huggingface.co/docs/diffusers/using-diffusers/loading) for more information.
```python
import torch
from diffusers import StableDiffusionPipeline
model_id = "n6ai/graphic-art"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "your prompt"
image = pipe(prompt).images[0]
image.save("./result.png")
``` |
yhavinga/gpt2-large-dutch | yhavinga | 2023-03-21T22:21:37Z | 169 | 6 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"gpt2-large",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt2-large
- gpt2
pipeline_tag: text-generation
datasets:
- yhavinga/mc4_nl_cleaned
---
# GPT2-Large pre-trained on cleaned Dutch mC4 🇳🇱
A GPT2 large model (762M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.
## How To Use
You can use this GPT2-model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt2-large-dutch'
from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPT2LMHeadModel.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer)
generated_text = generator('Het eiland West-', max_length=100, do_sample=True, top_k=40, top_p=0.95, repetition_penalty=2.0))
```
*"Het eiland West-" - "Terschelling wordt sinds jaar en dag bewoond door de mens. De mensen die in het huidige Terherne wonen doen er alles aan om hun dorp te behouden voor deze diersoort, namelijk; een natuurreservaat dat vooral bestaat uit hoge duinen met lage begroeing waar planten van vroeger worden afgewisseld (zoals wilde hyacinten)en waarop grassen groeien waarvan sommige soorten zeldzame vormen hebben ontwikkeld: duinlelie of blauwe bosbes zijn bijvoorbeeld bekend vanwege onder andere kleurmole"*
## Tokenizer
* BPE tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
## Dataset
This model was trained on of the `full` configuration (33B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except
* Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
* Sentences with less than 3 words are removed
* Sentences with a word of more than 1000 characters are removed
* Documents with less than 5 sentences are removed
* Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
"use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
## Models
TL;DR: [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) is the best model.
* The models with `a`/`b` in the step-column have been trained to step `a` of a total of `b` steps.
| | model | params | train seq len | ppl | loss | batch size | epochs | steps | optim | lr | duration | config |
|-----------------------------------------------------------------------------------|---------|--------|---------------|------|------|------------|--------|-----------------|-----------|--------|----------|-----------|
| [yhavinga/gpt-neo-125M-dutch](https://huggingface.co/yhavinga/gpt-neo-125M-dutch) | gpt neo | 125M | 512 | 20.9 | 3.04 | 128 | 1 | 190000/558608 | adam | 2.4e-3 | 1d 12h | full |
| [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) | gpt2 | 345M | 512 | 15.1 | 2.71 | 128 | 1 | 320000/520502 | adam | 8e-4 | 7d 2h | full |
| [yhavinga/gpt2-large-dutch](https://huggingface.co/yhavinga/gpt2-large-dutch) | gpt2 | 762M | 512 | 15.1 | 2.72 | 32 | 1 | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h | large |
| [yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) | gpt neo | 1.3B | 512 | 16.0 | 2.77 | 16 | 1 | 960000/3049896 | adafactor | 5e-4 | 7d 11h | full |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:
* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [gpt2-medium-persian](https://huggingface.co/flax-community/gpt2-medium-persian)
* [gpt2-medium-indonesian](https://huggingface.co/flax-community/gpt2-medium-persian)
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
|
bclavie/edubert | bclavie | 2023-03-21T22:21:17Z | 63 | 0 | transformers | [
"transformers",
"pytorch",
"education",
"learning analytics",
"educational data mining",
"en",
"arxiv:1912.00690",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2023-03-21T22:04:45Z | ---
license: mit
language:
- en
tags:
- education
- learning analytics
- educational data mining
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is the EduBERT model used in the [EduBERT: Pretrained Deep Language Models for Learning Analytics](https://arxiv.org/abs/1912.00690) from LAK20. It is a fine-tuned version of BERT-base on educational data.
## Model Description
We originally trained this model to support Learning Analytics task, showing it performed well on well-known educational text classification task.
## Bias, Risks, and Limitations
The model is provided as-is, and trained on the data described in the paper. Learning Analytics is a complex field, and decisions should not be taken fully automatically by models. This model should be used for analysis and to inform only.
## Citation
**BibTeX:**
```
@inproceedings{clavié2019edubert,
title={EduBERT: Pretrained Deep Language Models for Learning Analytics},
author={Benjamin Clavié and Kobi Gal},
year={2020},
booktitle={Companion Proceedings of the The 10th international Learning Analytics & Knowledge (LAK20)}
}
``` |
yhavinga/gpt2-medium-dutch | yhavinga | 2023-03-21T22:20:50Z | 914 | 3 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"gpt2-medium",
"nl",
"dataset:yhavinga/mc4_nl_cleaned",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language: nl
widget:
- text: "In het jaar 2030 zullen we"
- text: "Toen ik gisteren volledig in de ban was van"
- text: "Studenten en leraren van de Bogazici Universiteit in de Turkse stad Istanbul"
- text: "In Israël was een strenge lockdown"
tags:
- gpt2-medium
- gpt2
pipeline_tag: text-generation
datasets:
- yhavinga/mc4_nl_cleaned
---
# GPT2-Medium pre-trained on cleaned Dutch mC4 🇳🇱
A GPT2 medium-sized model (345M parameters) trained from scratch on Dutch, with perplexity 15.1 on cleaned Dutch mC4.
## How To Use
You can use this GPT2-model directly with a pipeline for text generation.
```python
MODEL_DIR='yhavinga/gpt2-medium-dutch'
from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPT2LMHeadModel.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':100})
generated_text = generator('In Antwerpen heeft zich gisteren', max_length=100, do_sample=True, top_k=40, top_p=0.95, repetition_penalty=2.0))
```
*"In Antwerpen heeft zich gisteren" - " een dramatische ontknoping voorgedaan in de Vlaamse deelregering. De VLD, die sinds afgelopen woensdag aan het bewind is in Vlaams-Waals gebied (de zogenaamde gewestelijke en niet rechtstreeks met Vlaanderen samenwerkende gewesten), krijgt toch geen meerderheidszetels bij verkiezingen voor gemeenteraadsverkiezingen in oktober of november volgend jaar in Westmalle, Berchem, Tervuren enz., aldus premier Jean-Pierre Van Cauwenberghe van Wallonië vandaag"*
## Tokenizer
* BPE tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
## Dataset
This model was trained on of the `full` configuration (33B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except
* Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
* Sentences with less than 3 words are removed
* Sentences with a word of more than 1000 characters are removed
* Documents with less than 5 sentences are removed
* Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
"use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
## Models
TL;DR: [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) is the best model.
* The models with `a`/`b` in the step-column have been trained to step `a` of a total of `b` steps.
| | model | params | train seq len | ppl | loss | batch size | epochs | steps | optim | lr | duration | config |
|-----------------------------------------------------------------------------------|---------|--------|---------------|------|------|------------|--------|-----------------|-----------|--------|----------|-----------|
| [yhavinga/gpt-neo-125M-dutch](https://huggingface.co/yhavinga/gpt-neo-125M-dutch) | gpt neo | 125M | 512 | 20.9 | 3.04 | 128 | 1 | 190000/558608 | adam | 2.4e-3 | 1d 12h | full |
| [yhavinga/gpt2-medium-dutch](https://huggingface.co/yhavinga/gpt2-medium-dutch) | gpt2 | 345M | 512 | 15.1 | 2.71 | 128 | 1 | 320000/520502 | adam | 8e-4 | 7d 2h | full |
| [yhavinga/gpt2-large-dutch](https://huggingface.co/yhavinga/gpt2-large-dutch) | gpt2 | 762M | 512 | 15.1 | 2.72 | 32 | 1 | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h | large |
| [yhavinga/gpt-neo-1.3B-dutch](https://huggingface.co/yhavinga/gpt-neo-1.3B-dutch) | gpt neo | 1.3B | 512 | 16.0 | 2.77 | 16 | 1 | 960000/3049896 | adafactor | 5e-4 | 7d 11h | full |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:
* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [gpt2-medium-persian](https://huggingface.co/flax-community/gpt2-medium-persian)
* [gpt2-medium-indonesian](https://huggingface.co/flax-community/gpt2-medium-persian)
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
|
BreadAi/MusePy-1-2 | BreadAi | 2023-03-21T22:04:53Z | 1,421 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"dataset:breadlicker45/musenet-encoders-12k",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-05T15:20:37Z | ---
datasets:
- breadlicker45/musenet-encoders-12k
--- |
BreadAi/MuseMini | BreadAi | 2023-03-21T22:04:37Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"dataset:breadlicker45/musenet-encoders-12k",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-08T21:58:49Z | ---
datasets:
- breadlicker45/musenet-encoders-12k
--- |
BreadAi/MuseCan-1-1 | BreadAi | 2023-03-21T22:04:01Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"dataset:breadlicker45/musenet-encoders-12k",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-13T10:49:01Z | ---
datasets:
- breadlicker45/musenet-encoders-12k
--- |
mrm8488/RuPERTa-base | mrm8488 | 2023-03-21T21:50:30Z | 23 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ---
language: es
thumbnail: https://i.imgur.com/DUlT077.jpg
widget:
- text: "España es un país muy <mask> en la UE"
---
# RuPERTa: the Spanish RoBERTa 🎃<img src="https://abs-0.twimg.com/emoji/v2/svg/1f1ea-1f1f8.svg" alt="spain flag" width="25"/>
RuPERTa-base (uncased) is a [RoBERTa model](https://github.com/pytorch/fairseq/tree/master/examples/roberta) trained on a *uncased* verison of [big Spanish corpus](https://github.com/josecannete/spanish-corpora).
RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data.
The architecture is the same as `roberta-base`:
`roberta.base:` **RoBERTa** using the **BERT-base architecture 125M** params
## Benchmarks 🧾
WIP (I continue working on it) 🚧
| Task/Dataset | F1 | Precision | Recall | Fine-tuned model | Reproduce it |
| -------- | ----: | --------: | -----: | --------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| POS | 97.39 | 97.47 | 97.32 | [RuPERTa-base-finetuned-pos](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pos) | [](https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/RuPERTa_base_finetuned_POS.ipynb)
| NER | 77.55 | 75.53 | 79.68 | [RuPERTa-base-finetuned-ner](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-ner) |
| SQUAD-es v1 | to-do | | |[RuPERTa-base-finetuned-squadv1](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-squadv1)
| SQUAD-es v2 | to-do | | |[RuPERTa-base-finetuned-squadv2](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-squadv2)
## Model in action 🔨
### Usage for POS and NER 🏷
```python
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
id2label = {
"0": "B-LOC",
"1": "B-MISC",
"2": "B-ORG",
"3": "B-PER",
"4": "I-LOC",
"5": "I-MISC",
"6": "I-ORG",
"7": "I-PER",
"8": "O"
}
tokenizer = AutoTokenizer.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
model = AutoModelForTokenClassification.from_pretrained('mrm8488/RuPERTa-base-finetuned-ner')
text ="Julien, CEO de HF, nació en Francia."
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
outputs = model(input_ids)
last_hidden_states = outputs[0]
for m in last_hidden_states:
for index, n in enumerate(m):
if(index > 0 and index <= len(text.split(" "))):
print(text.split(" ")[index-1] + ": " + id2label[str(torch.argmax(n).item())])
# Output:
'''
Julien,: I-PER
CEO: O
de: O
HF,: B-ORG
nació: I-PER
en: I-PER
Francia.: I-LOC
'''
```
For **POS** just change the `id2label` dictionary and the model path to [mrm8488/RuPERTa-base-finetuned-pos](https://huggingface.co/mrm8488/RuPERTa-base-finetuned-pos)
### Fast usage for LM with `pipelines` 🧪
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
model = AutoModelWithLMHead.from_pretrained('mrm8488/RuPERTa-base')
tokenizer = AutoTokenizer.from_pretrained("mrm8488/RuPERTa-base", do_lower_case=True)
from transformers import pipeline
pipeline_fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
pipeline_fill_mask("España es un país muy <mask> en la UE")
```
```json
[
{
"score": 0.1814306527376175,
"sequence": "<s> españa es un país muy importante en la ue</s>",
"token": 1560
},
{
"score": 0.024842597544193268,
"sequence": "<s> españa es un país muy fuerte en la ue</s>",
"token": 2854
},
{
"score": 0.02473250962793827,
"sequence": "<s> españa es un país muy pequeño en la ue</s>",
"token": 2948
},
{
"score": 0.023991240188479424,
"sequence": "<s> españa es un país muy antiguo en la ue</s>",
"token": 5240
},
{
"score": 0.0215945765376091,
"sequence": "<s> españa es un país muy popular en la ue</s>",
"token": 5782
}
]
```
## Acknowledgments
I thank [🤗/transformers team](https://github.com/huggingface/transformers) for answering my doubts and Google for helping me with the [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc) program.
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
juancopi81/GPT-Y | juancopi81 | 2023-03-21T21:39:48Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-11-23T15:59:07Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: GPT-Y
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. -->
# GPT-Y
This model is a fine-tuned version of [juancopi81/gpt2-finetuned-yannic-large](https://huggingface.co/juancopi81/gpt2-finetuned-yannic-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0797
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 403 | 3.0809 |
| 2.9847 | 2.0 | 806 | 3.0811 |
| 2.9516 | 3.0 | 1209 | 3.0781 |
| 2.916 | 4.0 | 1612 | 3.0791 |
| 2.9006 | 5.0 | 2015 | 3.0775 |
| 2.9006 | 6.0 | 2418 | 3.0799 |
| 2.8814 | 7.0 | 2821 | 3.0798 |
| 2.8672 | 8.0 | 3224 | 3.0797 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
deepsynthbody/deepfake_ecg | deepsynthbody | 2023-03-21T21:36:18Z | 30 | 14 | transformers | [
"transformers",
"pytorch",
"pulse2pulse-2",
"ECG",
"Synthetic ECG",
"unconditional-image-generation",
"custom_code",
"en",
"license:bsd",
"endpoints_compatible",
"region:us"
]
| unconditional-image-generation | 2023-02-24T17:08:37Z | ---
license: bsd
language:
- en
tags:
- ECG
- Synthetic ECG
pipeline_tag: unconditional-image-generation
---
# deepfake-ecg
[Paper](https://www.nature.com/articles/s41598-021-01295-2)
[GitHub](https://github.com/vlbthambawita/deepfake-ecg)
[Pre-generated ECGs (150k)](https://osf.io/6hved/)
---
# To generate synthetic ECGs from Hugging face
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("deepsynthbody/deepfake_ecg", trust_remote_code=True)
out = model(num_samples=5)
```
## [Pulse2Pulse - development repo](https://github.com/vlbthambawita/Pulse2Pulse)
If you want to train the model from scratch, please refere our development repository Pulse2Pulse.
---
## Usage
The generator functions can generate DeepFake ECGs with 8-lead values [lead names from first coloum to eighth colum: **'I','II','V1','V2','V3','V4','V5','V6'**] for 10s (5000 values per lead). These 8-leads format can be converted to 12-leads format using the following equations.
```
lead III value = (lead II value) - (lead I value)
lead aVR value = -0.5*(lead I value + lead II value)
lead aVL value = lead I value - 0.5 * lead II value
lead aVF value = lead II value - 0.5 * lead I value
```
### Pre-generated DeepFake ECGs and corresponding MUSE reports are here: https://osf.io/6hved/ or (https://huggingface.co/datasets/deepsynthbody/deepfake_ecg)
- In this repository, there are two DeepFake datasets:
1. 150k dataset - Randomly generated 150k DeepFakeECGs
2. Filtered all normals dataset - Only "Normal" ECGs filtered using the MUSE analysis report
## A real ECG vs a DeepFake ECG (from left to right):
## A sample DeepFake ECG:
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
## Citation:
```latex
@article{thambawita2021deepfake,
title={DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine},
author={Thambawita, Vajira and Isaksen, Jonas L and Hicks, Steven A and Ghouse, Jonas and Ahlberg, Gustav and Linneberg, Allan and Grarup, Niels and Ellervik, Christina and Olesen, Morten Salling and Hansen, Torben and others},
journal={Scientific reports},
volume={11},
number={1},
pages={1--8},
year={2021},
publisher={Nature Publishing Group}
}
```
## License
[MIT](https://choosealicense.com/licenses/mit/)
## For more details:
Please contact: [email protected], [email protected] |
YoungBeauty/xlm-roberta-base-finetuned-panx-it | YoungBeauty | 2023-03-21T21:35:46Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-03-21T20:23:57Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8175481754817548
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2617
- F1: 0.8175
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7926 | 1.0 | 70 | 0.3352 | 0.7479 |
| 0.2795 | 2.0 | 140 | 0.2678 | 0.8087 |
| 0.1795 | 3.0 | 210 | 0.2617 | 0.8175 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Emperor/ppo-Pyramids-1M | Emperor | 2023-03-21T21:30:46Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-21T21:30:41Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: Emperor/ppo-Pyramids-1M
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Emperor/ppo-Pyramids-3M | Emperor | 2023-03-21T21:29:22Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-03-21T21:29:15Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: Emperor/ppo-Pyramids-3M
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Robert-Bently/kr-eg-2 | Robert-Bently | 2023-03-21T21:28:08Z | 0 | 0 | diffusers | [
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-03-21T00:01:28Z | ---
license: creativeml-openrail-m
---
|
pfunk/PongNoFrameskip-v4-P_DQN-seed555 | pfunk | 2023-03-21T21:18:42Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T21:18:32Z | ---
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: 20.52 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **PongNoFrameskip-v4**
This is a trained model of a DQN agent playing PongNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[P_DQN]"
python -m cleanrl_utils.enjoy --exp-name P_DQN --env-id PongNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed555/raw/main/dqn_atari.py
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed555/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed555/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQN --seed 555
```
# Hyperparameters
```python
{'alg_type': 'dqn_atari.py',
'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'double_learning': False,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'P_DQN',
'exploration_fraction': 0.2,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 10000,
'max_gradient_norm': inf,
'save_model': True,
'seed': 555,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 5000000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
eLarry/rl_course_vizdoom_health_gathering_supreme | eLarry | 2023-03-21T21:05:32Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T21:04:58Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.05 +/- 2.89
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r eLarry/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
pfunk/PongNoFrameskip-v4-P_DQN-seed232 | pfunk | 2023-03-21T21:03:46Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T21:03:37Z | ---
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: 20.48 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **PongNoFrameskip-v4**
This is a trained model of a DQN agent playing PongNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[P_DQN]"
python -m cleanrl_utils.enjoy --exp-name P_DQN --env-id PongNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed232/raw/main/dqn_atari.py
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed232/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed232/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQN --seed 232
```
# Hyperparameters
```python
{'alg_type': 'dqn_atari.py',
'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'double_learning': False,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'P_DQN',
'exploration_fraction': 0.2,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 10000,
'max_gradient_norm': inf,
'save_model': True,
'seed': 232,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 5000000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
pfunk/PongNoFrameskip-v4-P_DQN-seed888 | pfunk | 2023-03-21T21:02:09Z | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T21:01:59Z | ---
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: 20.19 +/- 0.00
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **PongNoFrameskip-v4**
This is a trained model of a DQN agent playing PongNoFrameskip-v4.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQN.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[P_DQN]"
python -m cleanrl_utils.enjoy --exp-name P_DQN --env-id PongNoFrameskip-v4
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed888/raw/main/dqn_atari.py
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed888/raw/main/pyproject.toml
curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQN-seed888/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQN --seed 888
```
# Hyperparameters
```python
{'alg_type': 'dqn_atari.py',
'batch_size': 32,
'buffer_size': 1000000,
'capture_video': True,
'cuda': True,
'double_learning': False,
'end_e': 0.01,
'env_id': 'PongNoFrameskip-v4',
'exp_name': 'P_DQN',
'exploration_fraction': 0.2,
'gamma': 0.99,
'hf_entity': 'pfunk',
'learning_rate': 0.0001,
'learning_starts': 10000,
'max_gradient_norm': inf,
'save_model': True,
'seed': 888,
'start_e': 1.0,
'target_network_frequency': 1000,
'target_tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 5000000,
'track': True,
'train_frequency': 1,
'upload_model': True,
'wandb_entity': 'pfunk',
'wandb_project_name': 'dqpn'}
```
|
kucharskipj/Reinforce-CartPool | kucharskipj | 2023-03-21T21:02:09Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T21:01:56Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPool
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
|
nash0823/gpt2-physics | nash0823 | 2023-03-21T20:52:44Z | 41 | 0 | transformers | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-21T20:52:32Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: gpt2-physics
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gpt2-physics
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.27.2
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
MnLgt/strato-sofa | MnLgt | 2023-03-21T20:49:49Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-03-21T20:49:46Z | ---
license: mit
---
### Strato Sofa on Stable Diffusion
This is the `<strato-sofa>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
sophiadt/DialoGPT-medium-707 | sophiadt | 2023-03-21T20:48:36Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-12-19T22:58:33Z | ---
tags:
- conversational
---
# 707 AI Chat Bot
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on an otome game character, 707 from [Mystic Messenger](https://en.wikipedia.org/wiki/Mystic_Messenger). The data comes from [a Kaggle dataset of messages from the Deep Story in Mystic Messenger](https://www.kaggle.com/datasets/pineapplesoup/707-messages).
Credits to Lynn Zheng and her [Discord AI Chatbot tutorial](https://www.freecodecamp.org/news/discord-ai-chatbot/) for helping me build this.
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("sophiadt/DialoGPT-medium-707")
model = AutoModelWithLMHead.from_pretrained("sophiadt/DialoGPT-medium-707")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("707 Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
``` |
messham/taxi-25k-steps | messham | 2023-03-21T20:39:57Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T20:39:47Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-25k-steps
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="messham/taxi-25k-steps", 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"])
```
|
Yureeh/Reinforce-CartPole | Yureeh | 2023-03-21T20:37:18Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T20:37:09Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
IsaacBot/flan-t5-small-botco_QA-finetuned-question-generation-context-only | IsaacBot | 2023-03-21T20:35:43Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-03-21T20:15:53Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-small-botco_QA-finetuned-question-generation-context-only
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. -->
# flan-t5-small-botco_QA-finetuned-question-generation-context-only
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0952
- Rouge1: 83.429
- Rouge2: 80.1583
- Rougel: 80.2037
- Rougelsum: 83.2421
- Bleu-4: 62.4017
- Meteor: 87.7448
- Gen Len: 45.3499
## 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: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu-4 | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:-------:|
| 2.6539 | 2.33 | 50 | 2.2360 | 82.6627 | 79.5849 | 79.46 | 82.5244 | 57.5926 | 86.8675 | 49.0233 |
| 2.212 | 4.66 | 100 | 2.0952 | 83.429 | 80.1583 | 80.2037 | 83.2421 | 62.4017 | 87.7448 | 45.3499 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
messham/q-FrozenLake-v1-4x4-noSlippery | messham | 2023-03-21T20:33:57Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T20:33:49Z | ---
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="messham/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"])
```
|
Emperor/a2c-PandaReachDense-v2 | Emperor | 2023-03-21T20:02:16Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T19:59:52Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.97 +/- 0.70
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sarkerlab/SocBERT-final | sarkerlab | 2023-03-21T19:55:55Z | 8 | 2 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-02-09T19:05:49Z | # SocBERT model
Pretrained model on 20GB English tweets and 72GB Reddit comments using a masked language modeling (MLM) objective.
The tweets are from Archive and collected from Twitter Streaming API.
The Reddit comments are ramdonly sampled from all subreddits from 2015-2019.
SocBERT-base was pretrained on 819M sequence blocks for 100K steps.
SocBERT-final was pretrained on 929M (819M+110M) sequence blocks for 112K (100K+12K) steps.
We benchmarked SocBERT, on 40 text classification tasks with social media data.
The experiment results can be found in our paper:
```
@inproceedings{socbert:2023,
title = {{SocBERT: A Pretrained Model for Social Media Text}},
author = {Yuting Guo and Abeed Sarker},
booktitle = {Proceedings of the Fourth Workshop on Insights from Negative Results in NLP},
year = {2023}
}
```
A base version of the model can be found at [SocBERT-base](https://huggingface.co/sarkerlab/SocBERT-base). |
sarkerlab/SocBERT-base | sarkerlab | 2023-03-21T19:55:36Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-02-09T18:46:13Z | # SocBERT model
Pretrained model on 20GB English tweets and 72GB Reddit comments using a masked language modeling (MLM) objective.
The tweets are from Archive and collected from Twitter Streaming API.
The Reddit comments are ramdonly sampled from all subreddits from 2015-2019.
SocBERT-base was pretrained on 819M sequence blocks for 100K steps.
SocBERT-final was pretrained on 929M (819M+110M) sequence blocks for 112K (100K+12K) steps.
We benchmarked SocBERT, on 40 text classification tasks with social media data.
The experiment results can be found in our paper:
```
@inproceedings{socbert:2023,
title = {{SocBERT: A Pretrained Model for Social Media Text}},
author = {Yuting Guo and Abeed Sarker},
booktitle = {Proceedings of the Fourth Workshop on Insights from Negative Results in NLP},
year = {2023}
}
```
|
erikycd/chatbot_hadita | erikycd | 2023-03-21T19:41:21Z | 14 | 3 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"es",
"dataset:open_subtitles",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-06-30T20:14:31Z | ---
license: gpl-3.0
tags:
- conversational
- gpt2
language:
- es
datasets:
- open_subtitles
widget:
- text: Me gusta el deporte
example_title: Interacción
- text: Hola
example_title: Saludo
- text: ¿Como estas?
example_title: Pregunta
---
# Spanish GPT-2 as backbone
Fine-tuned model on Spanish language using [Opensubtitle](https://opus.nlpl.eu/OpenSubtitles-v2018.php) dataset. The original GPT-2
model was used as backbone which has been trained from scratch on the Spanish portion of OSCAR dataset, according to the [Flax/Jax](https://huggingface.co/flax-community/gpt-2-spanish)
Community by HuggingFace.
## Model description and fine tunning
First, the model used as backbone was the OpenAI's GPT-2, introduced in the paper "Language Models are Unsupervised Multitask Learners"
by Alec Radford et al. Second, transfer learning approach with a large dataset in Spanish was used to transform the text generation model to
conversational tasks. The use of special tokens plays a key role in the process of fine-tuning.
```python
tokenizer.add_special_tokens({"pad_token": "<pad>",
"bos_token": "<startofstring>",
"eos_token": "<endofstring>"})
tokenizer.add_tokens(["<bot>:"])
```
## How to use
You can use this model directly with a pipeline for auto model with casual LM:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("erikycd/chatbot_hadita")
model = AutoModelForCausalLM.from_pretrained("erikycd/chatbot_hadita")
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
model = model.to(device)
def infer(inp):
inp = "<startofstring> "+ inp +" <bot>: "
inp = tokenizer(inp, return_tensors = "pt")
X = inp["input_ids"].to(device)
attn = inp["attention_mask"].to(device)
output = model.generate(X, attention_mask = attn, pad_token_id = tokenizer.eos_token_id)
output = tokenizer.decode(output[0], skip_special_tokens = True)
return output
exit_commands = ('bye', 'quit')
text = ''
while text not in exit_commands:
text = input('\nUser: ')
output = infer(text)
print('Bot: ', output)
``` |
jon-tow/hh-gpt-j | jon-tow | 2023-03-21T19:37:30Z | 13 | 2 | transformers | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-17T16:22:25Z | ---
license: apache-2.0
language:
- en
---
GPT-J (with value head weights) trained on HH with PPO following [@reciprocated's](https://github.com/reciprocated) `trlx` example [here](https://github.com/CarperAI/trlx/blob/2f90ba0ecd640ae18cd62adb5e934a4b779f534b/examples/hh/ppo_hh.py).
- Dataset: [Dahoas/full-hh-rlhf](https://huggingface.co/datasets/Dahoas/full-hh-rlhf)
- Logs: https://wandb.ai/jon-tow/trlx/reports/hh-gpt-j--VmlldzozODE1NjAw
- Notebook: https://colab.research.google.com/drive/1B-XKZv7h6u_pkyvckGocukEX5zLmACqc
Usage:
```python
from transformers import AutoTokenizer
from trlx.models.modeling_ppo import AutoModelForCausalLMWithHydraValueHead
model = AutoModelForCausalLMWithHydraValueHead.from_pretrained("jon-tow/hh-gpt-j")
# original_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
prompt_1 = """\
Human: Hello, can you help me?
Assistant: Sure, what can I do for you?
Human: I'm looking for a good recipe for a strawberry cake. What ingredients do I need?
Assistant:\
"""
prompt_2 = """\
Human: Hi! What kind of music do you like?
Assistant: I like all kinds of music.
Human: I'm trying to learn how to play the guitar. Do you have any tips?
Assistant:\
"""
prompts = [prompt_1, prompt_2]
inputs = tokenizer(
[prompt_1, prompt_2],
return_tensors="pt",
padding=True,
)
samples = model.generate(
**inputs,
max_new_tokens=64,
top_k=0,
top_p=1.0,
do_sample=True,
)
responses = []
prompt_tokens_lengths = [len(tokenizer.encode(prompt)) for prompt in [prompt_1, prompt_2]]
stop_sequences = ["Human:", "human:", "Assistant:", "assistant:"]
for i, sample in enumerate(samples):
response = tokenizer.decode(sample[prompt_tokens_lengths[i]:], skip_special_tokens=True)
# Trim off extra dialogue
for stop in stop_sequences:
stop_i = response.find(stop)
if stop_i >= 0:
response = response[:stop_i].rstrip()
responses.append(response)
print()
for prompt, response in zip(prompts, responses):
print("=" * 40)
print(prompt + response)
print("=" * 40)
print()
```
Output:
```
========================================
Human: Hello, can you help me?
Assistant: Sure, what can I do for you?
Human: I'm looking for a good recipe for a strawberry cake. What ingredients do I need?
Assistant: Is strawberry flavour a primary flavour you want in the cake?
========================================
========================================
Human: Hi! What kind of music do you like?
Assistant: I like all kinds of music.
Human: I'm trying to learn how to play the guitar. Do you have any tips?
Assistant: One thing you can try is to form chords and strums. Form chords and strums will help you to practice and learn how to play instruments easily. You can also download free music online. Besure to check out different genres and instruments. You don't have to learn everyone all at once. Learning the basics is
========================================
``` |
lmp256/lora_sks_dogs | lmp256 | 2023-03-21T19:19:27Z | 0 | 0 | null | [
"paddlepaddle",
"stable-diffusion",
"stable-diffusion-ppdiffusers",
"text-to-image",
"ppdiffusers",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-03-21T19:19:16Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog in a bucket
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - lmp256/lora_sks_dogs
本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of sks dog in a bucket 文本进行了训练。 下面是在训练过程中生成的一些图片。
|
jorgeortizfuentes/spanish_hate_speech | jorgeortizfuentes | 2023-03-21T19:17:04Z | 104 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_keras_callback",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-03T19:31:53Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: spanish_hate_speech
results: []
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# spanish_hate_speech
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2 |
jorgeortizfuentes/spanish_incivility | jorgeortizfuentes | 2023-03-21T19:17:01Z | 100 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-03T19:10:42Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: spanish_incivility
results: []
pipeline_tag: text-classification
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# spanish_incivility
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2 |
MarinHinawa/DialoGPT-medium-Shintaro | MarinHinawa | 2023-03-21T19:14:14Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-03-21T02:42:17Z | ---
thumbnail:
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Light Novel Character
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("MarinHinawa/DialoGPT-medium-Shintaro")
model = AutoModelWithLMHead.from_pretrained("MarinHinawa/DialoGPT-medium-Shintaro")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("EneBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
``` |
Emperor/a2c-AntBulletEnv-v0 | Emperor | 2023-03-21T19:07:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T19:06:07Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1804.90 +/- 269.51
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
D0k-tor/rl_course_vizdoom_health_gathering_supreme | D0k-tor | 2023-03-21T19:06:13Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T19:06:04Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.47 +/- 4.07
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r D0k-tor/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
VMware/vbert-2021-base | VMware | 2023-03-21T18:56:15Z | 37 | 4 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"tensorflow",
"eng",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-05-11T22:33:48Z | ---
language:
- "eng"
thumbnail: "url to a thumbnail used in social sharing"
tags:
- "pytorch"
- "tensorflow"
license: "apache-2.0"
---
# vBERT-2021-BASE
### Model Info:
<ul>
<li> Authors: R&D AI Lab, VMware Inc.
<li> Model date: April, 2022
<li> Model version: 2021-base
<li> Model type: Pretrained language model
<li> License: Apache 2.0
</ul>
#### Motivation
Traditional BERT models struggle with VMware-specific words (Tanzu, vSphere, etc.), technical terms, and compound words. (<a href =https://medium.com/@rickbattle/weaknesses-of-wordpiece-tokenization-eb20e37fec99>Weaknesses of WordPiece Tokenization</a>)
We have pretrained our vBERT model to address the aforementioned issues using our <a href=https://medium.com/vmware-data-ml-blog/pretraining-a-custom-bert-model-6e37df97dfc4>BERT Pretraining Library</a>.
<br> We have replaced the first 1k unused tokens of BERT's vocabulary with VMware-specific terms to create a modified vocabulary. We then pretrained the 'bert-base-uncased' model for additional 78K steps (71k With MSL_128 and 7k with MSL_512) (approximately 5 epochs) on VMware domain data.
#### Intended Use
The model functions as a VMware-specific Language Model.
#### How to Use
Here is how to use this model to get the features of a given text in PyTorch:
```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('VMware/vbert-2021-base')
model = BertModel.from_pretrained("VMware/vbert-2021-base")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('VMware/vbert-2021-base')
model = TFBertModel.from_pretrained('VMware/vbert-2021-base')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Training
#### - Datasets
Publically available VMware text data such as VMware Docs, Blogs etc. were used for creating the pretraining corpus. Sourced in May, 2021. (~320,000 Documents)
#### - Preprocessing
<ul>
<li>Decoding HTML
<li>Decoding Unicode
<li>Stripping repeated characters
<li>Splitting compound word
<li>Spelling correction
</ul>
#### - Model performance measures
We benchmarked vBERT on various VMware-specific NLP downstream tasks (IR, classification, etc).
The model scored higher than the 'bert-base-uncased' model on all benchmarks.
### Limitations and bias
Since the model is further pretrained on the BERT model, it may have the same biases embedded within the original BERT model.
The data needs to be preprocessed using our internal vNLP Preprocessor (not available to the public) to maximize its performance.
|
VMware/t5-small-question-generator | VMware | 2023-03-21T18:55:21Z | 104 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"dataset:iarfmoose/question_generator",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-10-28T14:21:02Z | ---
license: apache-2.0
datasets: iarfmoose/question_generator
---
# T5-v1.1-Small Question Generator
## Model Description
This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output. It is based on Google's pretrained [T5-v1.1-Small](https://huggingface.co/google/t5-v1_1-small) model.
It was trained with and intended to be used with [AMontgomerie/question_generator](https://github.com/AMontgomerie/question_generator).
Observationally, end-to-end generation is a little over 2x faster with T5-Small compared to T5-Base; however, the quality of the questions is generally lower.
## Intended uses & limitations
The model is trained to generate reading comprehension-style questions with answers extracted from a text. The model performs best with full sentence answers, but can also be used with single word or short phrase answers.
### How to use
The model takes concatenated answers and context as an input sequence, and will generate a full question sentence as an output sequence. The max sequence length is 512 tokens. Inputs should be organized into the following format:
```
<answer> answer text here <context> context text here
```
The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.
For best results, a large number of questions can be generated, and then filtered using [iarfmoose/bert-base-cased-qa-evaluator](https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator).
For examples, please see https://github.com/iarfmoose/question_generator.
### Limitations and bias
The model is limited to generating questions in the same style as those found in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), [CoQA](https://stanfordnlp.github.io/coqa/), and [MSMARCO](https://microsoft.github.io/msmarco/). The generated questions can potentially be leading or reflect biases that are present in the context. If the context is too short or completely absent, or if the context and answer do not match, the generated question is likely to be incoherent.
## Training data
The model was fine-tuned on a dataset made up of several well-known QA datasets ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), [CoQA](https://stanfordnlp.github.io/coqa/), and [MSMARCO](https://microsoft.github.io/msmarco/)). The datasets were restructured by concatenating the answer and context fields into the previously-mentioned format. The question field from the datasets was used as the target during training. The [full training set](https://huggingface.co/datasets/iarfmoose/question_generator) was roughly 200,000 examples.
## Training procedure
The model was trained for 20 epochs over the training set with a learning rate of 1e-3. The batch size was kept at 4 to match the original training strategy used by [iarfmoose](https://huggingface.co/iarfmoose).
|
dball/whisper-medium-de-med | dball | 2023-03-21T18:46:14Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-02-19T16:37:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-de-med
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. -->
# whisper-medium-de-med
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0383
- Wer: 100.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 0.0001 | 40.0 | 1000 | 0.0383 | 100.0 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
k4black/roberta-base-e-snli-classification-nli-base | k4black | 2023-03-21T18:24:14Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:esnli",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-21T12:41:57Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- esnli
metrics:
- f1
- accuracy
model-index:
- name: roberta-base-e-snli-classification-nli-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: esnli
type: esnli
config: plain_text
split: validation
args: plain_text
metrics:
- name: F1
type: f1
value: 0.9108298866502319
- name: Accuracy
type: accuracy
value: 0.9109937004673847
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-e-snli-classification-nli-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the esnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2611
- F1: 0.9108
- Accuracy: 0.9110
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|
| 1.0317 | 0.05 | 400 | 0.5734 | 0.7771 | 0.7803 |
| 0.544 | 0.09 | 800 | 0.3994 | 0.8548 | 0.8555 |
| 0.4604 | 0.14 | 1200 | 0.3492 | 0.8681 | 0.8687 |
| 0.4235 | 0.19 | 1600 | 0.3323 | 0.8764 | 0.8777 |
| 0.3934 | 0.23 | 2000 | 0.3225 | 0.8831 | 0.8841 |
| 0.3863 | 0.28 | 2400 | 0.3086 | 0.8875 | 0.8872 |
| 0.3767 | 0.33 | 2800 | 0.2972 | 0.8892 | 0.8898 |
| 0.3726 | 0.37 | 3200 | 0.2910 | 0.8932 | 0.8936 |
| 0.3624 | 0.42 | 3600 | 0.2934 | 0.8934 | 0.8937 |
| 0.361 | 0.47 | 4000 | 0.2831 | 0.8989 | 0.8989 |
| 0.3553 | 0.51 | 4400 | 0.2905 | 0.8985 | 0.8993 |
| 0.3451 | 0.56 | 4800 | 0.2725 | 0.9019 | 0.9024 |
| 0.3475 | 0.61 | 5200 | 0.2712 | 0.9046 | 0.9051 |
| 0.3398 | 0.65 | 5600 | 0.2787 | 0.9024 | 0.9028 |
| 0.3322 | 0.7 | 6000 | 0.2697 | 0.9043 | 0.9046 |
| 0.3288 | 0.75 | 6400 | 0.2722 | 0.9006 | 0.9013 |
| 0.324 | 0.79 | 6800 | 0.2677 | 0.9066 | 0.9066 |
| 0.3335 | 0.84 | 7200 | 0.2629 | 0.9075 | 0.9077 |
| 0.3309 | 0.89 | 7600 | 0.2577 | 0.9058 | 0.9061 |
| 0.3236 | 0.93 | 8000 | 0.2561 | 0.9121 | 0.9121 |
| 0.3183 | 0.98 | 8400 | 0.2556 | 0.9084 | 0.9088 |
| 0.3022 | 1.03 | 8800 | 0.2668 | 0.9056 | 0.9064 |
| 0.2974 | 1.07 | 9200 | 0.2519 | 0.9087 | 0.9092 |
| 0.29 | 1.12 | 9600 | 0.2554 | 0.9103 | 0.9109 |
| 0.2855 | 1.16 | 10000 | 0.2611 | 0.9108 | 0.9110 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
aj-data/rotten_tomatoes_dataset | aj-data | 2023-03-21T17:55:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-03-21T17:54:31Z | ---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: rotten_tomatoes_dataset
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8630393996247655
---
<!-- 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. -->
# rotten_tomatoes_dataset
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7857
- Accuracy: 0.8630
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3923 | 1.0 | 1067 | 0.3781 | 0.8480 |
| 0.2186 | 2.0 | 2134 | 0.5862 | 0.8518 |
| 0.0747 | 3.0 | 3201 | 0.7857 | 0.8630 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
pszemraj/electra-base-discriminator-CoLA | pszemraj | 2023-03-21T17:49:45Z | 360 | 0 | transformers | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"electra",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-01-26T14:00:30Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: electra-base-discriminator-CoLA
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.6579677841732349
widget:
- text: The cat sat on the mat.
example_title: Correct grammatical sentence
- text: Me and my friend going to the store.
example_title: Incorrect subject-verb agreement
- text: I ain't got no money.
example_title: Incorrect verb conjugation and double negative
- text: She don't like pizza no more.
example_title: Incorrect verb conjugation and double negative
- text: They is arriving tomorrow.
example_title: Incorrect verb conjugation
---
# electra-base-discriminator-CoLA
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3542
- Matthews Correlation: 0.6580
## Model description
Trying to find a decent optimum between accuracy/quality and inference speed.
```json
{
"epoch": 8.0,
"eval_loss": 0.3541961908340454,
"eval_matthews_correlation": 0.6579677841732349,
"eval_runtime": 1.9552,
"eval_samples": 1043,
"eval_samples_per_second": 533.451,
"eval_steps_per_second": 33.756
}
```
## 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: 8e-05
- train_batch_size: 128
- eval_batch_size: 16
- seed: 22165
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 8.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4004 | 1.0 | 67 | 0.3569 | 0.6340 |
| 0.2843 | 2.0 | 134 | 0.3542 | 0.6580 |
| 0.1228 | 3.0 | 201 | 0.4201 | 0.6412 |
| 0.0989 | 4.0 | 268 | 0.4780 | 0.6757 |
| 0.0681 | 5.0 | 335 | 0.4900 | 0.6925 |
| 0.0506 | 6.0 | 402 | 0.5837 | 0.6785 |
| 0.0093 | 7.0 | 469 | 0.6298 | 0.6652 |
| 0.0244 | 8.0 | 536 | 0.6292 | 0.6750 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.1
|
TRiddle/default-LunarLander-v2 | TRiddle | 2023-03-21T17:44:39Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-03-21T17:44:07Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.10 +/- 18.62
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
...
```
|
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