modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
gouse-73/ppo-LunarLander-v2 | 2023-07-17T12:04:08.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | gouse-73 | null | null | gouse-73/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T12:03:50 | ---
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: 268.49 +/- 14.09
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
...
```
| 784 | [
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Oslaw/ppo-LunarLander-v2 | 2023-07-17T12:21:48.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Oslaw | null | null | Oslaw/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T12:21:26 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.52 +/- 15.66
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
...
```
| 784 | [
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karacam/ppo-LunarLander-v2 | 2023-07-17T13:56:13.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | karacam | null | null | karacam/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T13:55:53 | ---
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: 247.06 +/- 17.11
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
...
```
| 784 | [
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jeremyleejh/ppo-LunarLander-v2 | 2023-07-21T08:21:43.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jeremyleejh | null | null | jeremyleejh/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T14:08:31 | ---
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: 112.42 +/- 87.78
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
...
```
| 784 | [
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hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-bs-v4 | 2023-07-17T14:56:20.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | hafidikhsan | null | null | hafidikhsan/wav2vec2-large-xlsr-53-english-pronunciation-evaluation-bs-v4 | 0 | 2 | transformers | 2023-07-17T14:53:50 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wav2vec2-large-xlsr-53-english-pronunciation-evaluation-bs-v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-english-pronunciation-evaluation-bs-v4
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3181
- Accuracy: 0.79
- F1: 0.7920
- Precision: 0.7954
- Recall: 0.79
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.14 | 1.0 | 2000 | 0.9878 | 0.604 | 0.5956 | 0.6041 | 0.604 |
| 1.3551 | 2.0 | 4000 | 1.0238 | 0.636 | 0.6261 | 0.6489 | 0.636 |
| 0.7984 | 3.0 | 6000 | 1.0629 | 0.748 | 0.7475 | 0.7494 | 0.748 |
| 0.6879 | 4.0 | 8000 | 1.2007 | 0.772 | 0.7733 | 0.7750 | 0.772 |
| 0.0593 | 5.0 | 10000 | 1.2298 | 0.796 | 0.7979 | 0.8011 | 0.796 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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peterdamn/speecht5_finetuned_voxpopuli_nl | 2023-07-17T15:47:39.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"text-to-speech",
"generated_from_trainer",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | peterdamn | null | null | peterdamn/speecht5_finetuned_voxpopuli_nl | 0 | 2 | transformers | 2023-07-17T15:04:46 | ---
license: mit
tags:
- text-to-speech
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
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[
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nthngdy/headless-pythia-owt2-70m-raw | 2023-09-20T13:54:34.000Z | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"dataset:the_pile_openwebtext2",
"arxiv:2309.08351",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | nthngdy | null | null | nthngdy/headless-pythia-owt2-70m-raw | 0 | 2 | transformers | 2023-07-17T15:19:36 | ---
license: mit
datasets:
- the_pile_openwebtext2
language:
- en
pipeline_tag: text-generation
---
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** TBD
- **Paper:** https://arxiv.org/abs/2309.08351
### Model Architecture and Objective
This model is a Pythia-70m architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective.
#### Software
[More Information Needed]
## Citation
**BibTeX:**
```bibtex
@misc{godey2023headless,
title={Headless Language Models: Learning without Predicting with Contrastive Weight Tying},
author={Nathan Godey and Éric de la Clergerie and Benoît Sagot},
year={2023},
eprint={2309.08351},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Model Card Authors
Nathan Godey
Eric de la Clergerie
Benoît Sagot
## Model Card Contact
nathan.godey@inria.fr
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mj-718/ppo-LunarLander-v2 | 2023-07-17T17:36:48.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mj-718 | null | null | mj-718/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T17:36:26 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.24 +/- 16.62
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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pcapp/ppo-Pyramids-Training | 2023-07-17T17:56:05.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | pcapp | null | null | pcapp/ppo-Pyramids-Training | 0 | 2 | ml-agents | 2023-07-17T17:56:01 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: pcapp/ppo-Pyramids-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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dariowsz/ppo-LunarLander-v2 | 2023-07-17T18:03:08.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | dariowsz | null | null | dariowsz/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T18:02:47 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 243.32 +/- 37.66
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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xoyeop/distilbert-base-uncased-DIALOCONAN-CLS | 2023-07-17T21:25:54.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | xoyeop | null | null | xoyeop/distilbert-base-uncased-DIALOCONAN-CLS | 0 | 2 | transformers | 2023-07-17T18:19:56 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-DIALOCONAN-CLS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-DIALOCONAN-CLS
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3391
- Precision: 0.7050
- Recall: 0.7076
- F1: 0.7062
- Accuracy: 0.9404
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3266 | 1.0 | 2500 | 0.3531 | 0.6899 | 0.6891 | 0.6884 | 0.9162 |
| 0.1862 | 2.0 | 5000 | 0.3141 | 0.7056 | 0.7078 | 0.7065 | 0.9407 |
| 0.0775 | 3.0 | 7500 | 0.3391 | 0.7050 | 0.7076 | 0.7062 | 0.9404 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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chandan9t8/ppo-Pyramid | 2023-07-17T18:31:27.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | chandan9t8 | null | null | chandan9t8/ppo-Pyramid | 0 | 2 | ml-agents | 2023-07-17T18:31:24 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: chandan9t8/ppo-Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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monideep2255/spell_correction_M05_LM | 2023-07-17T19:15:32.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | monideep2255 | null | null | monideep2255/spell_correction_M05_LM | 0 | 2 | transformers | 2023-07-17T18:31:35 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: spell_correction_M05_LM
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. -->
# spell_correction_M05_LM
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 274 | 0.2890 |
| 1.8446 | 2.0 | 548 | 0.0540 |
| 1.8446 | 3.0 | 822 | 0.0403 |
| 0.028 | 4.0 | 1096 | 0.0344 |
| 0.028 | 5.0 | 1370 | 0.0289 |
| 0.0137 | 6.0 | 1644 | 0.0289 |
| 0.0137 | 7.0 | 1918 | 0.0283 |
| 0.0063 | 8.0 | 2192 | 0.0266 |
| 0.0063 | 9.0 | 2466 | 0.0271 |
| 0.0043 | 10.0 | 2740 | 0.0272 |
| 0.0033 | 11.0 | 3014 | 0.0281 |
| 0.0033 | 12.0 | 3288 | 0.0264 |
| 0.003 | 13.0 | 3562 | 0.0277 |
| 0.003 | 14.0 | 3836 | 0.0274 |
| 0.003 | 15.0 | 4110 | 0.0265 |
| 0.003 | 16.0 | 4384 | 0.0290 |
| 0.0024 | 17.0 | 4658 | 0.0276 |
| 0.0024 | 18.0 | 4932 | 0.0270 |
| 0.0025 | 19.0 | 5206 | 0.0276 |
| 0.0025 | 20.0 | 5480 | 0.0272 |
| 0.0016 | 21.0 | 5754 | 0.0271 |
| 0.0018 | 22.0 | 6028 | 0.0272 |
| 0.0018 | 23.0 | 6302 | 0.0282 |
| 0.0014 | 24.0 | 6576 | 0.0276 |
| 0.0014 | 25.0 | 6850 | 0.0283 |
| 0.0014 | 26.0 | 7124 | 0.0280 |
| 0.0014 | 27.0 | 7398 | 0.0279 |
| 0.0013 | 28.0 | 7672 | 0.0280 |
| 0.0013 | 29.0 | 7946 | 0.0282 |
| 0.0014 | 30.0 | 8220 | 0.0281 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.12.1+cu102
- Datasets 2.13.1
- Tokenizers 0.13.3
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bwilkie/ppo-LunarLander-v2 | 2023-07-17T19:36:52.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | bwilkie | null | null | bwilkie/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T19:36:32 | ---
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: 264.67 +/- 19.74
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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tgsc/debertina-base | 2023-10-18T21:43:08.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"deberta",
"deberta-v3",
"pt",
"pt-br",
"dataset:allenai/c4",
"arxiv:2111.09543",
"arxiv:2003.10555",
"arxiv:2006.03654",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | tgsc | null | null | tgsc/debertina-base | 0 | 2 | transformers | 2023-07-17T20:16:58 | ---
language: pt
tags:
- deberta
- deberta-v3
- pt
- pt-br
datasets:
- allenai/c4
library_name: transformers
license: mit
---
# DeBERTina
<p align="center">
<img src="https://huggingface.co/tgsc/debertina-base/resolve/main/DeBERTina.png" alt="DeBERTina"/>
</p>
DeBERTina é um modelo [DeBERTa-v3](https://arxiv.org/abs/2111.09543) em português treinado no estilo [ELECTRA](https://arxiv.org/abs/2003.10555), com RTD (Replaced Token Detection) e *gradient-disentangled embedding sharing* (GDES).
*DeBERTina is a portuguese [DeBERTa-v3](https://arxiv.org/abs/2111.09543) model trained electra-style [ELECTRA](https://arxiv.org/abs/2003.10555) (with Replaced Token Detection - RTD) and gradient-disentangled embedding sharing (GDES).*
| Model | type | Vocabulary | Backbone + Embeddings = Total Parameters |
| :-: | :-: | :-: | :-: |
| [ult5-pt-small](https://huggingface.co/tgsc/ult5-pt-small) | encoder-decoder | 65k | 56.6M + 25.8M = 82.4M |
| [sentence-transformer-ult5-pt-small](https://huggingface.co/tgsc/sentence-transformer-ult5-pt-small) | sentence-transformer | 65k | 25.2 + 25.8M = 51M |
| [DeBERTina-base](https://huggingface.co/tgsc/debertina-base) | encoder | 32k | 85.5M + 24.6M = 110.0M |
| [DeBERTina-base-128k-vocab](https://huggingface.co/tgsc/debertina-base-128k-vocab) | encoder | 128k | 85.5M + 98.3M = 183.8M |
| [DeBERTina-large](https://huggingface.co/tgsc/debertina-large) | encoder | 128k | 348.4M + 98.3M = 433.9.0M |
| [DeBERTina-xsmall](https://huggingface.co/tgsc/debertina-xsmall) | encoder | 128k | 21.5M + 49.2M = 70.6M |
- **Developed by:** Thacio Garcia Scandaroli
- **Model type:** DeBERTa-v3
- **Language(s) (NLP):** Português
- **License:** MIT
Benchmarks e tutorial de fine-tune: [https://github.com/thacio/LLM-Notebooks](https://github.com/thacio/LLM-Notebooks)
*Benchmarks e fine-tune notebook*: [https://github.com/thacio/LLM-Notebooks](https://github.com/thacio/LLM-Notebooks)
Special tokens:
'[PAD]', '[CLS]', '[SEP]', '[UNK]'
## Treino
O modelo foi treinado com o corpus C4 em português, utilizando um tokenizer sentencepiece com vocabulário de tamanho 128k.
O treino consiste em um gerador e um discriminador. O gerador é treinado com *masked language modeling* em 15% dos tokens. Em seguida, tokens são substituídos pelas
predições do gerador, e o discriminador é treinado de forma a identificar quais tokens são originais e quais foram substítudos.
*The model was trained with the C4 corpus in portuguese with a sentencepiece tokenizer with a vocabulary of 128.*
*The training is done with a generator and a discriminator. The generator is trained with maskeed language modeling as BERT, but without next sentence prediction, by masking 15% of the tokens.*
*The masked tokens are then replaced by the generators prediction, and the discriminator is trained with the objective of identifying the which are the original and replaced tokens.*
## Fine-tunning
O fine-tunning é feito com o discriminador.
Para carregar o modelo para classificações:
*Fine-tunning should be done with the discrimnator.*
*Loading the model for classification:*
```python
from transformers import AutoModelForSequenceClassification
num_labels = 2 # number of labels in classes
model = AutoModelForSequenceClassification.from_pretrained("tgsc/debertina-base",num_labels=num_labels)
```
## Citation
``` latex
@inproceedings{
2023debertina,
title={DeBERTina: A portuguese DeBERTa-v3 model.},
author = {Thacio Garcia Scandaroli},
year={2023},
url={https://huggingface.co/tgsc/debertina-base}
}
```
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
The DeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has only 86M backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 2.0 and MNLI tasks.
| Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)|
|-------------------|----------|-------------------|-----------|----------|
| RoBERTa-base |50 |86 | 83.7/80.5 | 87.6/- |
| XLNet-base |32 |92 | -/80.2 | 86.8/- |
| ELECTRA-base |30 |86 | -/80.5 | 88.8/ |
| DeBERTa-base |50 |100 | 86.2/83.1| 88.8/88.5|
| DeBERTa-v3-base |128|86 | **88.4/85.4** | **90.6/90.7**|
| DeBERTa-v3-base + SiFT |128|86 | -/- | 91.0/-|
We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
#### Fine-tuning with HF transformers
```bash
#!/bin/bash
cd transformers/examples/pytorch/text-classification/
pip install datasets
export TASK_NAME=mnli
output_dir="ds_results"
num_gpus=8
batch_size=8
python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
run_glue.py \
--model_name_or_path microsoft/deberta-v3-base \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--evaluation_strategy steps \
--max_seq_length 256 \
--warmup_steps 500 \
--per_device_train_batch_size ${batch_size} \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir $output_dir \
--overwrite_output_dir \
--logging_steps 1000 \
--logging_dir $output_dir
```
### Citation
If you find DeBERTa useful for your work, please cite the following papers:
``` latex
@misc{he2021debertav3,
title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
year={2021},
eprint={2111.09543},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
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0.01288604736328125,
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tijstijs/ppo-LunarLander-v2 | 2023-07-17T20:23:56.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | tijstijs | null | null | tijstijs/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T20:23:34 | ---
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: 256.64 +/- 23.67
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
...
```
| 784 | [
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ozzzzz/ppo-LunarLander-v2 | 2023-07-17T21:32:37.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ozzzzz | null | null | ozzzzz/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T21:32:14 | ---
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: 258.43 +/- 25.36
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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jwb220/PPO-LunarLander-v2 | 2023-07-17T22:31:20.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jwb220 | null | null | jwb220/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-17T22:31:01 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.16 +/- 29.76
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
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0.0199127197265625,
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kingducks/ppo-LunarLander-v2 | 2023-07-18T00:05:27.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | kingducks | null | null | kingducks/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T00:04:53 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MLP PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.94 +/- 16.90
name: mean_reward
verified: false
---
# **MLP PPO** Agent playing **LunarLander-v2**
This is a trained model of a **MLP 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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vphu123/whisper_totaldataa2 | 2023-09-24T05:59:44.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | vphu123 | null | null | vphu123/whisper_totaldataa2 | 0 | 2 | transformers | 2023-07-18T03:18:29 | ---
tags:
- whisper-event
- generated_from_trainer
model-index:
- name: whisper-base-lastversion
results: []
metrics:
- wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-base-lastversion
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1732
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: gpu
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 80000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3116 | 1 | 5000 | 0.6231 |
| 0.2104 | 3 | 10000 | 0.4287 |
| 0.1729 | 4 | 15000 | 0.3421 |
| 0.1472 | 6 | 20000 | 0.3211 |
| 0.128 | 7 | 25000 | 0.2811 |
| 0.1065 | 9 | 30000 | 0.2649 |
| 0.0995 | 10 | 35000 | 0.2523 |
| 0.0812 | 12 | 40000 | 0.2401 |
| 0.066 | 14 | 45000 | 0.2311 |
| 0.0574 | 15 | 50000 | 0.2132 |
| 0.0463 | 17 | 55000 | 0.2077 |
| 0.04 | 18 | 60000 | 0.1957 |
| 0.0314 | 19 | 65000 | 0.1813 |
| 0.0305 | 20 | 70000 | 0.1802 |
| 0.0298 | 21 | 75000 | 0.1755 |
| 0.0265 | 22 | 80000 | 0.1732 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0a0+gitcc01568
- Datasets 2.13.1
- Tokenizers 0.13.3 | 2,002 | [
[
-0.0291595458984375,
-0.034759521484375,
0.0027008056640625,
0.00873565673828125,
-0.017791748046875,
-0.0304107666015625,
-0.006317138671875,
-0.018951416015625,
0.019561767578125,
0.0288238525390625,
-0.05657958984375,
-0.052032470703125,
-0.04632568359375,
... |
SeDm/ppo-LunarLander-v2 | 2023-07-18T07:07:54.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | SeDm | null | null | SeDm/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T07:07:32 | ---
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: 258.41 +/- 20.79
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
SeDm/ppo-LunarLander-v2-gamma03 | 2023-07-18T08:02:29.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | SeDm | null | null | SeDm/ppo-LunarLander-v2-gamma03 | 0 | 2 | stable-baselines3 | 2023-07-18T08:02:08 | ---
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: -88.68 +/- 154.18
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
...
```
| 785 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
1daniar/ppo-LunarLander-v2 | 2023-07-18T12:18:30.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 1daniar | null | null | 1daniar/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T08:28:28 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.04 +/- 18.55
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
sarahflan/distilbert-base-uncased-finetuned-as_sentences_fewshot | 2023-07-18T08:42:08.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | sarahflan | null | null | sarahflan/distilbert-base-uncased-finetuned-as_sentences_fewshot | 0 | 2 | transformers | 2023-07-18T08:34:42 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-as_sentences_fewshot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-as_sentences_fewshot
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0227
- Accuracy: 0.9933
- F1: 0.9933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6953 | 1.0 | 11 | 0.6832 | 0.6267 | 0.5993 |
| 0.6562 | 2.0 | 22 | 0.5071 | 0.9267 | 0.9268 |
| 0.4346 | 3.0 | 33 | 0.1365 | 0.9933 | 0.9933 |
| 0.1714 | 4.0 | 44 | 0.0566 | 0.9933 | 0.9933 |
| 0.1125 | 5.0 | 55 | 0.0234 | 1.0 | 1.0 |
| 0.0897 | 6.0 | 66 | 0.0264 | 0.9933 | 0.9933 |
| 0.0487 | 7.0 | 77 | 0.0465 | 0.9867 | 0.9867 |
| 0.0401 | 8.0 | 88 | 0.0082 | 1.0 | 1.0 |
| 0.0364 | 9.0 | 99 | 0.0273 | 0.9933 | 0.9933 |
| 0.0237 | 10.0 | 110 | 0.0163 | 0.9933 | 0.9933 |
| 0.0209 | 11.0 | 121 | 0.0044 | 1.0 | 1.0 |
| 0.0196 | 12.0 | 132 | 0.0056 | 1.0 | 1.0 |
| 0.0198 | 13.0 | 143 | 0.0059 | 1.0 | 1.0 |
| 0.0047 | 14.0 | 154 | 0.0063 | 1.0 | 1.0 |
| 0.0157 | 15.0 | 165 | 0.0115 | 0.9933 | 0.9933 |
| 0.0142 | 16.0 | 176 | 0.0116 | 0.9933 | 0.9933 |
| 0.0035 | 17.0 | 187 | 0.0111 | 0.9933 | 0.9933 |
| 0.0028 | 18.0 | 198 | 0.0114 | 0.9933 | 0.9933 |
| 0.0023 | 19.0 | 209 | 0.0103 | 0.9933 | 0.9933 |
| 0.0019 | 20.0 | 220 | 0.0102 | 0.9933 | 0.9933 |
| 0.0016 | 21.0 | 231 | 0.0117 | 0.9933 | 0.9933 |
| 0.0016 | 22.0 | 242 | 0.0103 | 0.9933 | 0.9933 |
| 0.0014 | 23.0 | 253 | 0.0072 | 0.9933 | 0.9933 |
| 0.0014 | 24.0 | 264 | 0.0059 | 0.9933 | 0.9933 |
| 0.0013 | 25.0 | 275 | 0.0071 | 0.9933 | 0.9933 |
| 0.0012 | 26.0 | 286 | 0.0079 | 0.9933 | 0.9933 |
| 0.0012 | 27.0 | 297 | 0.0076 | 0.9933 | 0.9933 |
| 0.0011 | 28.0 | 308 | 0.0076 | 0.9933 | 0.9933 |
| 0.001 | 29.0 | 319 | 0.0085 | 0.9933 | 0.9933 |
| 0.0009 | 30.0 | 330 | 0.0088 | 0.9933 | 0.9933 |
| 0.001 | 31.0 | 341 | 0.0089 | 0.9933 | 0.9933 |
| 0.0009 | 32.0 | 352 | 0.0092 | 0.9933 | 0.9933 |
| 0.0009 | 33.0 | 363 | 0.0091 | 0.9933 | 0.9933 |
| 0.0008 | 34.0 | 374 | 0.0100 | 0.9933 | 0.9933 |
| 0.0021 | 35.0 | 385 | 0.0312 | 0.9933 | 0.9933 |
| 0.0008 | 36.0 | 396 | 0.0340 | 0.9933 | 0.9933 |
| 0.0009 | 37.0 | 407 | 0.0313 | 0.9933 | 0.9933 |
| 0.0008 | 38.0 | 418 | 0.0278 | 0.9933 | 0.9933 |
| 0.0008 | 39.0 | 429 | 0.0246 | 0.9933 | 0.9933 |
| 0.0008 | 40.0 | 440 | 0.0226 | 0.9933 | 0.9933 |
| 0.0007 | 41.0 | 451 | 0.0212 | 0.9933 | 0.9933 |
| 0.0007 | 42.0 | 462 | 0.0200 | 0.9933 | 0.9933 |
| 0.0007 | 43.0 | 473 | 0.0241 | 0.9933 | 0.9933 |
| 0.0007 | 44.0 | 484 | 0.0249 | 0.9933 | 0.9933 |
| 0.0007 | 45.0 | 495 | 0.0244 | 0.9933 | 0.9933 |
| 0.0007 | 46.0 | 506 | 0.0238 | 0.9933 | 0.9933 |
| 0.0007 | 47.0 | 517 | 0.0234 | 0.9933 | 0.9933 |
| 0.0006 | 48.0 | 528 | 0.0230 | 0.9933 | 0.9933 |
| 0.0007 | 49.0 | 539 | 0.0227 | 0.9933 | 0.9933 |
| 0.0007 | 50.0 | 550 | 0.0227 | 0.9933 | 0.9933 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 4,939 | [
[
-0.03619384765625,
-0.0421142578125,
0.012786865234375,
0.005115509033203125,
-0.00022554397583007812,
0.008148193359375,
0.0033016204833984375,
0.002262115478515625,
0.05352783203125,
0.0247802734375,
-0.045501708984375,
-0.04541015625,
-0.048553466796875,
... |
AndrewMay/ppo-LunarLander-v2 | 2023-07-18T08:44:18.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | AndrewMay | null | null | AndrewMay/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T08:43:59 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.35 +/- 22.45
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
...
```
| 784 | [
[
-0.00020754337310791016,
-0.027130126953125,
0.0170745849609375,
0.023345947265625,
-0.006053924560546875,
0.0027618408203125,
0.034423828125,
-0.01213836669921875,
0.0199127197265625,
0.06500244140625,
-0.043182373046875,
-0.03521728515625,
-0.0343017578125,
... |
sufyn/ppo-LunarLander-v2 | 2023-07-18T09:16:30.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sufyn | null | null | sufyn/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T09:16:10 | ---
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: 262.34 +/- 24.09
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
...
```
| 784 | [
[
-0.00020754337310791016,
-0.027130126953125,
0.0170745849609375,
0.023345947265625,
-0.006053924560546875,
0.0027618408203125,
0.034423828125,
-0.01213836669921875,
0.0199127197265625,
0.06500244140625,
-0.043182373046875,
-0.03521728515625,
-0.0343017578125,
... |
sherif1311/flan-t5-base-reviewb-text-classification | 2023-07-18T11:11:59.000Z | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | sherif1311 | null | null | sherif1311/flan-t5-base-reviewb-text-classification | 0 | 2 | transformers | 2023-07-18T09:50:13 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: flan-t5-base-reviewb-text-classification
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-base-reviewb-text-classification
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2081
- F1: 76.3399
- Gen Len: 2.3170
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1+cu116
- Datasets 2.13.1
- Tokenizers 0.12.1
| 1,198 | [
[
-0.026092529296875,
-0.031707763671875,
0.0093841552734375,
0.0015306472778320312,
-0.0223388671875,
-0.030853271484375,
-0.01450347900390625,
-0.02984619140625,
0.009796142578125,
0.023529052734375,
-0.03802490234375,
-0.046905517578125,
-0.05364990234375,
... |
mrmrob003/ppo-LunarLander-v2 | 2023-07-18T11:00:07.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mrmrob003 | null | null | mrmrob003/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T10:16:10 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.13 +/- 17.18
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
giuseppemassafra/ppo-LunarLander-v2 | 2023-07-18T10:16:37.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | giuseppemassafra | null | null | giuseppemassafra/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T10:16:17 | ---
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: 268.78 +/- 10.40
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
MikeFisher/ppo-LunarLander-v2 | 2023-07-18T10:48:03.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | MikeFisher | null | null | MikeFisher/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T10:47:41 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.12 +/- 16.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
saharad/ppo-LunarLander-v2 | 2023-07-18T10:51:14.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | saharad | null | null | saharad/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T10:50:55 | ---
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: 270.68 +/- 16.37
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
...
```
| 784 | [
[
-0.00023484230041503906,
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0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
Mgollen/PPO-Lunarlander-v2 | 2023-07-18T12:28:38.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Mgollen | null | null | Mgollen/PPO-Lunarlander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T12:28:19 | ---
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: 259.89 +/- 19.91
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
...
```
| 784 | [
[
-0.00023484230041503906,
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0.023345947265625,
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0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
crcdng/Pyramids | 2023-07-18T12:43:49.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | crcdng | null | null | crcdng/Pyramids | 0 | 2 | ml-agents | 2023-07-18T12:43:46 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: crcdng/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,327 | [
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0.0301666259765625,
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... |
lokpalai/lokpalgpt-falcon-7b-lora-4.5 | 2023-07-18T13:32:44.000Z | [
"transformers",
"pytorch",
"RefinedWebModel",
"text-generation",
"custom_code",
"en",
"license:cc-by-4.0",
"text-generation-inference",
"region:us"
] | text-generation | lokpalai | null | null | lokpalai/lokpalgpt-falcon-7b-lora-4.5 | 0 | 2 | transformers | 2023-07-18T12:46:47 | ---
language:
- en
inference: true
widget:
- text: "What are the duties of the President of India as per the Constitution?"
example_title: "Duties of President"
- text: "Can you analyze the legal implications of the Ayodhya Verdict by the Supreme Court of India?"
example_title: "Implications of Ayodhya Verdict"
- text: "Can you summarize the main provisions of the Hindu Succession Act, 1956?"
example_title: "Diving Top 10"
- text: "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nDevelop a legal strategy for a client based on the facts of the provided case.\n\n### Input:\nThe client in question is a government company that terminated the services of a permanent employee without providing any justification. The termination was carried out by invoking a rule similar to Rule 9(i) in the Central Inland Water Transport Corporation Ltd. vs Brojo Nath Ganguly & Anr. case. The employee who was terminated has taken legal action by challenging both the termination order and the validity of the rule in the High Court under Article 226.\n\n### Response:\n"
example_title: "Create Legal Strategy 1"
- text: "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction: \nDevelop a legal strategy for a hypothetical client based on the facts of the provided case.\n\n### Input:\nThe individual seeking assistance is a research scientist employed at a government-funded research institute, comparable to CSIR. They have been unjustly dismissed from their position and seek to contest the termination through legal means. The individual contends that the institute, being government-funded, qualifies as a 'State' as per Article 12 of the Constitution. Consequently, they believe they should have the right to file a writ petition against the institute.\n\n### Response:\n"
example_title: "Create Legal Strategy 2"
- text: "What is DV act ?"
example_title: "Understand Act"
license: cc-by-4.0
---
# LokPalAI: Bridging the Gap to Legal Empowerment
LokPalAI is an advanced language model finetuned for Indian scenarios, specifically designed to bridge the gap between individuals and legal empowerment. With LokPalAI, users can interact with a powerful query box to seek information and guidance related to Indian law.
## Features:
1. Interact with LokPalAI’s Query Box: LokPalAI provides a user-friendly query box interface where users can input their legal queries and receive accurate and relevant responses. Whether you need information about a specific law, legal procedure, or any other legal matter, LokPalAI is here to assist you.
2. Enhanced with Rail Guards: To ensure the accuracy and reliability of the information provided, LokPalAI incorporates rail guards. These safeguards help prevent the generation of misleading or incorrect legal advice. We understand the importance of reliable legal information, and our rail guards are designed to maintain the highest standards of accuracy.
3. Real-Time Responses using RAG: LokPalAI leverages the Retrieve and Generate (RAG) framework to provide real-time responses to your legal queries. RAG combines the power of retrieval-based models with generation-based models, ensuring that the information provided is both contextually relevant and up to date.
4. Thorough Testing and Maintenance: We understand the criticality of maintaining a reliable and accurate legal information system. LokPalAI undergoes extensive testing to ensure its performance and reliability. We continuously monitor and update the model to account for changes in Indian law, ensuring that the information provided is always accurate and up to date.
# ✨ LokpalGPT-Instruct-Falcon-7b
## Dataset
The dataset is being curated and created using judgements available in IndianKanoon.com. You can refer the whole process here. Soon, we will be releasing our dataset and the training process.
## How to Use for Inference ?
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with LokpalGPT-Instruct-Falcon-7b.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "lokpalai/lokpalgpt-falcon-7b-lora-4.5"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Can you analyze the legal implications of the Ayodhya Verdict by the Supreme Court of India?",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
temperature=0.5,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
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msladic/a2c-PandaReachDense-v2 | 2023-07-18T13:13:38.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | msladic | null | null | msladic/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T13:04:25 | ---
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: -2.77 +/- 0.61
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
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
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0.0469970703125,
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0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
kyars/ppo-LunarLander-v2 | 2023-07-18T13:29:02.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | kyars | null | null | kyars/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T13:28:42 | ---
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: 246.31 +/- 78.16
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
...
```
| 784 | [
[
-0.00023484230041503906,
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0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
tztztztztz/mlpppo-lunarLander-V2 | 2023-07-18T14:08:17.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | tztztztztz | null | null | tztztztztz/mlpppo-lunarLander-V2 | 0 | 2 | stable-baselines3 | 2023-07-18T14:07:59 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo_mlp
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.69 +/- 25.49
name: mean_reward
verified: false
---
# **ppo_mlp** Agent playing **LunarLander-v2**
This is a trained model of a **ppo_mlp** 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
...
```
| 796 | [
[
-0.004520416259765625,
-0.0259552001953125,
0.0148773193359375,
0.0271148681640625,
0.002899169921875,
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0.0301361083984375,
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0.0211029052734375,
0.064208984375,
-0.045928955078125,
-0.034576416015625,
-0.038421630859375... |
Oslaw/a2c-AntBulletEnv-v0 | 2023-07-18T14:21:44.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Oslaw | null | null | Oslaw/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-18T14:19:00 | ---
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: 1351.27 +/- 262.90
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
...
```
| 791 | [
[
-0.02679443359375,
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0.0106964111328125,
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veluchs/speecht5_finetuned_voxpopuli_it_partial | 2023-07-24T08:39:55.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | veluchs | null | null | veluchs/speecht5_finetuned_voxpopuli_it_partial | 0 | 2 | transformers | 2023-07-18T15:31:58 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_it_partial
results: []
pipeline_tag: text-to-speech
---
<!-- 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. -->
# speecht5_finetuned_voxpopuli_it_partial
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on a part of the italian voxpopuli dataset.
It was finetuned as part of the HF Audio Course.
It achieves the following results on the evaluation set:
- Loss: 0.4955
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5461 | 17.39 | 1000 | 0.5055 |
| 0.5222 | 34.78 | 2000 | 0.4958 |
| 0.5125 | 52.17 | 3000 | 0.4955 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 | 1,626 | [
[
-0.034088134765625,
-0.0428466796875,
0.000023066997528076172,
0.007049560546875,
-0.0189056396484375,
-0.0232086181640625,
-0.0159759521484375,
-0.012451171875,
-0.005077362060546875,
0.0179595947265625,
-0.051513671875,
-0.04931640625,
-0.039794921875,
-0.... |
YojitShinde/a2c-AntBulletEnv-v0 | 2023-07-20T13:40:41.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | YojitShinde | null | null | YojitShinde/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-18T16:05:32 | ---
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: 2083.21 +/- 52.79
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
...
```
| 790 | [
[
-0.02679443359375,
-0.04443359375,
0.0106964111328125,
0.0208892822265625,
-0.0034961700439453125,
0.0018033981323242188,
0.0187530517578125,
-0.0176544189453125,
0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
Jyotiyadav/NER-bert-base-multilingual-uncased | 2023-07-18T16:41:47.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Jyotiyadav | null | null | Jyotiyadav/NER-bert-base-multilingual-uncased | 0 | 2 | transformers | 2023-07-18T16:39:38 | This model is trained on bert-base-uncased & Dataset - https://drive.google.com/file/d/1hyXTTubD9CRjL1MBSIU_iVxFCdtGXqgB/view?usp=sharing &
Notebook - https://colab.research.google.com/drive/1zHrs3hosTXBPiy0P1O-x6Z2EQ9RZGUsQ?usp=sharing
| Label | Precision | Recall | F1-Score | Support |
|--------------------|-----------|--------|----------|---------|
| commodity | 0.50 | 0.50 | 0.50 | 66 |
| company | 0.79 | 0.87 | 0.83 | 164 |
| delivery_cap | 0.00 | 0.00 | 0.00 | 10 |
| delivery_location | 0.58 | 0.27 | 0.37 | 26 |
| delivery_port | 0.88 | 0.90 | 0.89 | 332 |
| delivery_state | 0.80 | 0.82 | 0.81 | 45 |
| incoterms | 0.88 | 0.97 | 0.92 | 187 |
| measures | 0.91 | 0.96 | 0.94 | 802 |
| package_type | 0.89 | 0.96 | 0.92 | 292 |
| pickup_cap | 0.83 | 0.97 | 0.90 | 139 |
| pickup_location | 0.77 | 0.92 | 0.84 | 356 |
| pickup_port | 0.00 | 0.00 | 0.00 | 3 |
| pickup_state | 0.83 | 0.82 | 0.83 | 67 |
| quantity | 0.93 | 0.90 | 0.92 | 199 |
| stackable | 0.91 | 0.84 | 0.87 | 57 |
| volume | 0.46 | 0.67 | 0.55 | 69 |
| weight | 0.81 | 0.83 | 0.82 | 247 |
|--------------------|-----------|--------|----------|---------|
| micro avg | 0.84 | 0.90 | 0.87 | 3061 |
| macro avg | 0.69 | 0.72 | 0.70 | 3061 |
| weighted avg | 0.84 | 0.90 | 0.87 | 3061 |
| 1,756 | [
[
-0.0207366943359375,
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0.005435943603515625,
0.0193939208984375,
-0.0172882080078125,
-0.01593017578125,
0.0002582073211669922,
-0.0155029296875,
0.02362060546875,
0.0195159912109375,
-0.050506591796875,
-0.051544189453125,
-0.04547119140625,
... |
TitanML/ct2-int8-open-llama-7b | 2023-07-26T16:15:48.000Z | [
"transformers",
"llama",
"text-generation",
"dataset:togethercomputer/RedPajama-Data-1T",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | TitanML | null | null | TitanML/ct2-int8-open-llama-7b | 0 | 2 | transformers | 2023-07-18T20:25:11 | ---
license: apache-2.0
datasets:
- togethercomputer/RedPajama-Data-1T
---
# OpenLLaMA: An Open Reproduction of LLaMA
In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a 7B and 3B model trained on 1T tokens, as well as the preview of a 13B model trained on 600B tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details.
## Weights Release, License and Usage
We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license.
### Loading the Weights with Hugging Face Transformers
Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that the auto-converted fast tokenizer sometimes gives incorrect tokenizations.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage.
```python
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
model_path = 'openlm-research/open_llama_3b'
# model_path = 'openlm-research/open_llama_7b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float16, device_map='auto',
)
prompt = 'Q: What is the largest animal?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=32
)
print(tokenizer.decode(generation_output[0]))
```
For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama).
### Evaluating with LM-Eval-Harness
The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below:
```python
tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained(
pretrained if tokenizer is None else tokenizer,
revision=revision + ("/" + subfolder if subfolder is not None else ""),
use_fast=False
)
```
### Loading the Weights with EasyLM
For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. Note that we use BOS (beginning of sentence) token (id=1) during training, so it is best to prepend this token for best performance during few-shot evaluation.
## Dataset and Training
We train our models on the [RedPajama](https://www.together.xyz/blog/redpajama) dataset released by [Together](https://www.together.xyz/), which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA.
We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and [fully sharded data parallelism (also know as ZeRO stage 3)](https://engineering.fb.com/2021/07/15/open-source/fsdp/) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model.
## Evaluation
We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks.
| **Task/Metric** | GPT-J 6B | LLaMA 7B | OpenLLaMA 7B | OpenLLaMA 3B | OpenLLaMA 13B 600BT |
| ---------------------- | -------- | -------- | ------------ | ------------ | ------------------- |
| anli_r1/acc | 0.32 | 0.35 | 0.33 | 0.33 | 0.33 |
| anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.32 | 0.35 |
| anli_r3/acc | 0.35 | 0.37 | 0.38 | 0.35 | 0.38 |
| arc_challenge/acc | 0.34 | 0.39 | 0.37 | 0.34 | 0.39 |
| arc_challenge/acc_norm | 0.37 | 0.41 | 0.38 | 0.37 | 0.42 |
| arc_easy/acc | 0.67 | 0.68 | 0.72 | 0.69 | 0.74 |
| arc_easy/acc_norm | 0.62 | 0.52 | 0.68 | 0.65 | 0.70 |
| ddboolq/acc | 0.50 | 0.56 | 0.53 | 0.49 | 0.71 |
| hellaswag/acc | 0.36 | 0.36 | 0.63 | 0.43 | 0.54 |
| hellaswag/acc_norm | 0.66 | 0.73 | 0.72 | 0.67 | 0.73 |
| openbookqa/acc | 0.29 | 0.29 | 0.30 | 0.27 | 0.30 |
| openbookqa/acc_norm | 0.38 | 0.41 | 0.40 | 0.40 | 0.41 |
| piqa/acc | 0.75 | 0.78 | 0.76 | 0.75 | 0.77 |
| piqa/acc_norm | 0.76 | 0.78 | 0.77 | 0.76 | 0.78 |
| record/em | 0.88 | 0.91 | 0.89 | 0.88 | 0.90 |
| record/f1 | 0.89 | 0.91 | 0.90 | 0.89 | 0.90 |
| rte/acc | 0.54 | 0.56 | 0.60 | 0.58 | 0.65 |
| truthfulqa_mc/mc1 | 0.20 | 0.21 | 0.23 | 0.22 | 0.22 |
| truthfulqa_mc/mc2 | 0.36 | 0.34 | 0.35 | 0.35 | 0.35 |
| wic/acc | 0.50 | 0.50 | 0.51 | 0.48 | 0.49 |
| winogrande/acc | 0.64 | 0.68 | 0.67 | 0.62 | 0.67 |
| Average | 0.51 | 0.53 | 0.55 | 0.52 | 0.56 |
We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.
## Contact
We would love to get feedback from the community. If you have any questions, please open an issue or contact us.
OpenLLaMA is developed by:
[Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research.
*Equal Contribution
## Acknowledgment
We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback.
The OpenLLaMA 13B model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support.
## Reference
If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
```
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
| 10,507 | [
[
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hector981/ppo-LunarLander-v2 | 2023-07-18T21:32:44.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | hector981 | null | null | hector981/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T21:32:20 | ---
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: 236.70 +/- 24.97
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.034454345703125,
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0.06500244140625,
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-... |
acdg1214/ppo-LunarLander-v2 | 2023-07-18T21:46:09.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | acdg1214 | null | null | acdg1214/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T21:45:48 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.77 +/- 14.25
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
...
```
| 784 | [
[
-0.00023484230041503906,
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0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.06500244140625,
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-0.0343017578125,
-... |
neuromax/LunarLander-v2-rl-unit-1 | 2023-07-18T21:51:04.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | neuromax | null | null | neuromax/LunarLander-v2-rl-unit-1 | 0 | 2 | stable-baselines3 | 2023-07-18T21:50:41 | ---
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: 237.06 +/- 28.50
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
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aroot/eng-deu-tok_budget_random | 2023-07-18T22:25:59.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | aroot | null | null | aroot/eng-deu-tok_budget_random | 0 | 2 | transformers | 2023-07-18T22:12:17 | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-tok_budget_random
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. -->
# eng-deu-tok_budget_random
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6856
- Bleu: 20.4422
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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Falcinspire/ppo-LunarLander-v2 | 2023-07-18T22:39:33.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Falcinspire | null | null | Falcinspire/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-18T22:13:05 | ---
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: 282.08 +/- 17.86
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
...
```
| 784 | [
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0.06500244140625,
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-... |
giocs2017/ppo-PyramisTraining | 2023-07-19T00:03:03.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | giocs2017 | null | null | giocs2017/ppo-PyramisTraining | 0 | 2 | ml-agents | 2023-07-19T00:02:57 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: giocs2017/ppo-PyramisTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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jrad98/ppo-LunarLander-v2 | 2023-07-19T00:13:30.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jrad98 | null | null | jrad98/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T00:13:11 | ---
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: 262.28 +/- 23.13
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
...
```
| 784 | [
[
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-0.0343017578125,
... |
jrad98/ppo-LunarLander-v2_1 | 2023-07-19T00:27:16.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jrad98 | null | null | jrad98/ppo-LunarLander-v2_1 | 0 | 2 | stable-baselines3 | 2023-07-19T00:26:55 | ---
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: -128.10 +/- 31.63
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 785 | [
[
-0.00020432472229003906,
-0.0271453857421875,
0.0170745849609375,
0.0233612060546875,
-0.00604248046875,
0.002777099609375,
0.034454345703125,
-0.01215362548828125,
0.0199127197265625,
0.06500244140625,
-0.043121337890625,
-0.035247802734375,
-0.034332275390625,... |
BAAI/AquilaCode-multi | 2023-07-24T00:47:10.000Z | [
"transformers",
"pytorch",
"aquila",
"custom_code",
"license:other",
"endpoints_compatible",
"region:us"
] | null | BAAI | null | null | BAAI/AquilaCode-multi | 3 | 2 | transformers | 2023-07-19T01:31:24 | ---
license: other
---
<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/BAAI/AquilaCode-multi/blob/main/README_zh.md">简体中文</a> |
<p>
</h4>
Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations.
- 🌟 **Supports open source commercial licenses**. The source code of the Aquila series models is based on the [Apache 2.0 agreement](https://www.apache.org/licenses/LICENSE-2.0), while the model weight is based on the [BAAI Aquila Model License Agreement](https://huggingface.co/BAAI/AquilaCode-multi/blob/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf). Users can use it for commercial purposes as long as they meet the licensing restrictions.
- ✍️ **Possesses Chinese and English knowledge**. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge.
- 👮♀️ **Complies with domestic data regulations**. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models.
- 🎯 **Continuous improvements and open sourcing**. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions.
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the [FlagAI GitHub repository](https://github.com/FlagAI-Open/FlagAI/), [FlagAI's Zhihu account](https://www.zhihu.com/people/95-22-20-18) and [FlagAI's official technical communication group](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg).
| Model | Model Type | Description | Status | GPUs Used |
| :----------------- | :----------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :--------------| :----------- |
| Aquila-7B | Base model, 7 billion parameters | **Aquila Base Model** inherits the architectural design advantages of GPT-3 and LLaMA. It replaces a batch of more efficient underlying operator implementations, redesigns the implementation of bilingual tokenizer, upgrades BMTrain parallel training method, and achieves nearly 8 times the training efficiency of Magtron+DeepSpeed ZeRO-2. | Released | Nvidia-A100 |
| Aquila-33B | Base model, 33 billion parameters | Same as above | Coming soon | Nvidia-A100 |
| AquilaChat-7B | SFT model, fine-tuned and RL based on Aquila-7B | **AquilaChat Dialog Model** supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source **[AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) multimodal language image generation model** of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's **InstructFace multi-step controllable text-picture model**, it is easy to achieve multi-step controllable editing of human face images. | Released | Nvidia-A100 |
| AquilaChat-33B | SFT model, fine-tuned and RL based on Aquila-33B | Same as above | Coming soon | Nvidia-A100 |
| AquilaCode-multi | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B. | AquilaCode utilizes high-quality, filtered, and compliant open-source code data for training, with a dataset size of approximately 10-40% compared to other open-source code generation models. By following the provided official guidelines, developers can harness the power of the AquilaCode model to customize their own code assistant. | Released | Nvidia-A100 |
| AquilaCode-py | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B, trained on Horizon Robotics chips | Same as above | Released | Nvidia-A100 |
We will continue to release improved versions of Aquila model as open source.
- 2023/07/24 ：release v0.9
- AquilaCode-mutil-01 md5: e6ea49fea7a737ffe41086ec7019cebb
- AquilaCode-mutil-02 md5: 4bba98eac44d785358ed5b6d2144a94a
- AquilaCode-Python-01 md5: e202e5b82db773ea369fe843fef1c34c
- AquilaCode-Python-02 md5: 3923b2b020e2af71755b11248076437f
Aquila-7B v0.8 has shown improvements in the FlagEval large model evaluation ("Objective") compared to version 0.7. It achieved improvements of approximately 10.07% on MMLU_Chinese, 14.84% on TruthfulQA, and 7.94% on MMLU datasets. For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn.
For detailed version change history, see [Change Log](https://huggingface.co/BAAI/Aquila-7B/blob/main/change_log.log).
## Quick Start Aquila-7B
### 1. Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_info = "BAAI/AquilaCode-multi"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
model.eval()
model.to("cuda:3")
text = "#补全代码\ndef quick_sort(x):"
tokens = tokenizer.encode_plus(text)['input_ids'][:-1]
tokens = torch.tensor(tokens)[None,].to("cuda:3")
with torch.no_grad():
out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007)[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
```
## License
Aquila-7B and AquilaChat-33B open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaCode-multi/blob/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf) | 8,010 | [
[
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0.01473236083984375,
0.022735595703125,
-0.0133514404296875,
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-0.01105499267578125,
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0.0003979206085205078,
0.0228118896484375,
-0.043914794921875,
-0.026458740234375,
-0.02697753906... |
BAAI/AquilaCode-py | 2023-07-24T00:47:26.000Z | [
"transformers",
"pytorch",
"aquila",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | BAAI | null | null | BAAI/AquilaCode-py | 2 | 2 | transformers | 2023-07-19T01:37:02 | ---
license: other
---
<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/BAAI/AquilaCode-py/blob/main/README_zh.md">简体中文</a> |
<p>
</h4>
Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations.
- 🌟 **Supports open source commercial licenses**. The source code of the Aquila series models is based on the [Apache 2.0 agreement](https://www.apache.org/licenses/LICENSE-2.0), while the model weight is based on the [BAAI Aquila Model License Agreement](https://huggingface.co/BAAI/AquilaCode-py/blob/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf). Users can use it for commercial purposes as long as they meet the licensing restrictions.
- ✍️ **Possesses Chinese and English knowledge**. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge.
- 👮♀️ **Complies with domestic data regulations**. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models.
- 🎯 **Continuous improvements and open sourcing**. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions.
The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the [FlagAI GitHub repository](https://github.com/FlagAI-Open/FlagAI/), [FlagAI's Zhihu account](https://www.zhihu.com/people/95-22-20-18) and [FlagAI's official technical communication group](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg).
| Model | Model Type | Description | Status | GPUs Used |
| :----------------- | :----------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :--------------| :----------- |
| Aquila-7B | Base model, 7 billion parameters | **Aquila Base Model** inherits the architectural design advantages of GPT-3 and LLaMA. It replaces a batch of more efficient underlying operator implementations, redesigns the implementation of bilingual tokenizer, upgrades BMTrain parallel training method, and achieves nearly 8 times the training efficiency of Magtron+DeepSpeed ZeRO-2. | Released | Nvidia-A100 |
| Aquila-33B | Base model, 33 billion parameters | Same as above | Coming soon | Nvidia-A100 |
| AquilaChat-7B | SFT model, fine-tuned and RL based on Aquila-7B | **AquilaChat Dialog Model** supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source **[AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) multimodal language image generation model** of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's **InstructFace multi-step controllable text-picture model**, it is easy to achieve multi-step controllable editing of human face images. | Released | Nvidia-A100 |
| AquilaChat-33B | SFT model, fine-tuned and RL based on Aquila-33B | Same as above | Coming soon | Nvidia-A100 |
| AquilaCode-multi | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B. | AquilaCode utilizes high-quality, filtered, and compliant open-source code data for training, with a dataset size of approximately 10-40% compared to other open-source code generation models. By following the provided official guidelines, developers can harness the power of the AquilaCode model to customize their own code assistant. | Released | Nvidia-A100 |
| AquilaCode-py | Base model, "text-code" generation model, continue-pre-trained based on Aquila-7B, trained on Horizon Robotics chips | Same as above | Released | Nvidia-A100 |
We will continue to release improved versions of Aquila model as open source.
- 2023/07/24 ：release v0.9
- AquilaCode-mutil-01 md5: e202e5b82db773ea369fe843fef1c34c
- AquilaCode-mutil-02 md5: 3923b2b020e2af71755b11248076437f
- AquilaCode-Python-01 md5: e202e5b82db773ea369fe843fef1c34c
- AquilaCode-Python-02 md5: 3923b2b020e2af71755b11248076437f
Aquila-7B v0.8 has shown improvements in the FlagEval large model evaluation ("Objective") compared to version 0.7. It achieved improvements of approximately 10.07% on MMLU_Chinese, 14.84% on TruthfulQA, and 7.94% on MMLU datasets. For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn.
For detailed version change history, see [Change Log](https://huggingface.co/BAAI/Aquila-7B/blob/main/change_log.log).
## Quick Start Aquila-7B
### 1. Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_info = "BAAI/AquilaCode-py"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
model.eval()
model.to("cuda:4")
text = "#补全代码\ndef quick_sort(x):"
tokens = tokenizer.encode_plus(text)['input_ids'][:-1]
tokens = torch.tensor(tokens)[None,].to("cuda:4")
with torch.no_grad():
out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007)[0]
out = tokenizer.decode(out.cpu().numpy().tolist())
print(out)
```
## License
Aquila-7B and AquilaChat-33B open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaCode-py/blob/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf) | 7,999 | [
[
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0.014892578125,
0.0225067138671875,
-0.01305389404296875,
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-0.01142120361328125,
-0.03826904296875,
0.00005507469177246094,
0.023223876953125,
-0.044036865234375,
-0.0268707275390625,
-0.0266265869140625... |
jamesx66/ppo-LunarLander-v2 | 2023-07-19T02:37:58.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jamesx66 | null | null | jamesx66/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T02:37:36 | ---
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: 238.65 +/- 20.59
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
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-... |
AltairXz/ppo-LunarLander-v2 | 2023-07-19T06:36:25.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | AltairXz | null | null | AltairXz/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T06:35:48 | ---
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: -196.46 +/- 52.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 785 | [
[
-0.00020432472229003906,
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0.0199127197265625,
0.06500244140625,
-0.043121337890625,
-0.035247802734375,
-0.034332275390625,... |
albertHu/temp-model | 2023-07-19T06:46:39.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | albertHu | null | null | albertHu/temp-model | 0 | 2 | stable-baselines3 | 2023-07-19T06:46:17 | ---
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: 173.21 +/- 35.99
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
...
```
| 784 | [
[
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-... |
marcoi/vit-base-patch16-224-finetuned-flower | 2023-07-19T08:08:25.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | image-classification | marcoi | null | null | marcoi/vit-base-patch16-224-finetuned-flower | 0 | 2 | transformers | 2023-07-19T07:56:11 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: vit-base-patch16-224-finetuned-flower
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned-flower
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.13.3
| 1,119 | [
[
-0.0307769775390625,
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0.007091522216796875,
0.0204315185546875,
-0.030059814453125,
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0.00707244873046875,
0.0233001708984375,
-0.057830810546875,
-0.036407470703125,
-0.0434265136718... |
Oslaw/a2c-PandaReachDense-v2 | 2023-07-19T08:13:17.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Oslaw | null | null | Oslaw/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T08:10:25 | ---
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: -2.64 +/- 0.77
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
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
-0.0249481201171875,
0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
shayonhuggingface/videberta-sentiment-analysis | 2023-07-19T16:44:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:vietnamese_students_feedback",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | shayonhuggingface | null | null | shayonhuggingface/videberta-sentiment-analysis | 0 | 2 | transformers | 2023-07-19T08:38:36 | ---
base_model: Fsoft-AIC/videberta-xsmall
tags:
- generated_from_trainer
datasets:
- vietnamese_students_feedback
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: videberta-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: vietnamese_students_feedback
type: vietnamese_students_feedback
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9470198675496688
- name: Precision
type: precision
value: 0.9480840543881335
- name: Recall
type: recall
value: 0.9527950310559006
- name: F1
type: f1
value: 0.9504337050805451
---
<!-- 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. -->
# videberta-sentiment-analysis
This model is a fine-tuned version of [Fsoft-AIC/videberta-xsmall](https://huggingface.co/Fsoft-AIC/videberta-xsmall) on the vietnamese_students_feedback dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2787
- Accuracy: 0.9470
- Precision: 0.9481
- Recall: 0.9528
- F1: 0.9504
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6152 | 0.58 | 100 | 0.4777 | 0.8007 | 0.8580 | 0.7503 | 0.8005 |
| 0.408 | 1.16 | 200 | 0.3241 | 0.8669 | 0.8943 | 0.8509 | 0.8721 |
| 0.3268 | 1.74 | 300 | 0.2726 | 0.8954 | 0.8837 | 0.9255 | 0.9041 |
| 0.2654 | 2.33 | 400 | 0.2296 | 0.9199 | 0.9212 | 0.9292 | 0.9252 |
| 0.253 | 2.91 | 500 | 0.2088 | 0.9159 | 0.9206 | 0.9217 | 0.9212 |
| 0.2014 | 3.49 | 600 | 0.2318 | 0.9172 | 0.9028 | 0.9466 | 0.9242 |
| 0.1939 | 4.07 | 700 | 0.2131 | 0.9212 | 0.9224 | 0.9304 | 0.9264 |
| 0.1698 | 4.65 | 800 | 0.2005 | 0.9311 | 0.9499 | 0.9193 | 0.9343 |
| 0.1822 | 5.23 | 900 | 0.2249 | 0.9245 | 0.9089 | 0.9540 | 0.9309 |
| 0.1441 | 5.81 | 1000 | 0.2038 | 0.9311 | 0.9311 | 0.9404 | 0.9357 |
| 0.1403 | 6.4 | 1100 | 0.2044 | 0.9338 | 0.9315 | 0.9453 | 0.9383 |
| 0.1377 | 6.98 | 1200 | 0.1991 | 0.9417 | 0.9567 | 0.9329 | 0.9447 |
| 0.1191 | 7.56 | 1300 | 0.2955 | 0.9119 | 0.8792 | 0.9677 | 0.9213 |
| 0.1227 | 8.14 | 1400 | 0.2362 | 0.9318 | 0.9199 | 0.9553 | 0.9372 |
| 0.1023 | 8.72 | 1500 | 0.2221 | 0.9358 | 0.9286 | 0.9528 | 0.9405 |
| 0.1049 | 9.3 | 1600 | 0.1940 | 0.9424 | 0.9454 | 0.9466 | 0.9460 |
| 0.1002 | 9.88 | 1700 | 0.1949 | 0.9404 | 0.9649 | 0.9217 | 0.9428 |
| 0.0946 | 10.47 | 1800 | 0.2232 | 0.9404 | 0.9625 | 0.9242 | 0.9430 |
| 0.0911 | 11.05 | 1900 | 0.2016 | 0.9457 | 0.9641 | 0.9329 | 0.9482 |
| 0.0818 | 11.63 | 2000 | 0.2636 | 0.9311 | 0.9128 | 0.9627 | 0.9371 |
| 0.0889 | 12.21 | 2100 | 0.2279 | 0.9450 | 0.9524 | 0.9441 | 0.9482 |
| 0.0668 | 12.79 | 2200 | 0.2460 | 0.9411 | 0.9409 | 0.9491 | 0.9450 |
| 0.0635 | 13.37 | 2300 | 0.2764 | 0.9424 | 0.9465 | 0.9453 | 0.9459 |
| 0.072 | 13.95 | 2400 | 0.2519 | 0.9437 | 0.9390 | 0.9565 | 0.9477 |
| 0.0697 | 14.53 | 2500 | 0.2705 | 0.9404 | 0.9408 | 0.9478 | 0.9443 |
| 0.0602 | 15.12 | 2600 | 0.2686 | 0.9450 | 0.9513 | 0.9453 | 0.9483 |
| 0.065 | 15.7 | 2700 | 0.2629 | 0.9450 | 0.9501 | 0.9466 | 0.9484 |
| 0.0628 | 16.28 | 2800 | 0.2644 | 0.9450 | 0.9547 | 0.9416 | 0.9481 |
| 0.0505 | 16.86 | 2900 | 0.2704 | 0.9424 | 0.9400 | 0.9528 | 0.9463 |
| 0.0471 | 17.44 | 3000 | 0.2787 | 0.9470 | 0.9481 | 0.9528 | 0.9504 |
| 0.0568 | 18.02 | 3100 | 0.2766 | 0.9450 | 0.9424 | 0.9553 | 0.9488 |
| 0.0523 | 18.6 | 3200 | 0.2659 | 0.9424 | 0.9421 | 0.9503 | 0.9462 |
| 0.0487 | 19.19 | 3300 | 0.3091 | 0.9338 | 0.9222 | 0.9565 | 0.9390 |
| 0.0529 | 19.77 | 3400 | 0.3575 | 0.9272 | 0.9045 | 0.9652 | 0.9339 |
| 0.0484 | 20.35 | 3500 | 0.3228 | 0.9358 | 0.9214 | 0.9615 | 0.9410 |
| 0.0456 | 20.93 | 3600 | 0.2694 | 0.9437 | 0.9412 | 0.9540 | 0.9476 |
| 0.0424 | 21.51 | 3700 | 0.2793 | 0.9404 | 0.9376 | 0.9516 | 0.9445 |
| 0.045 | 22.09 | 3800 | 0.2953 | 0.9417 | 0.9356 | 0.9565 | 0.9459 |
| 0.0395 | 22.67 | 3900 | 0.2840 | 0.9417 | 0.9377 | 0.9540 | 0.9458 |
| 0.0418 | 23.26 | 4000 | 0.3527 | 0.9305 | 0.9108 | 0.9640 | 0.9366 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| 5,733 | [
[
-0.04302978515625,
-0.0352783203125,
0.0256195068359375,
0.01117706298828125,
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0.0032291412353515625,
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0.046478271484375,
0.0286407470703125,
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susnato/speecht5_finetuned_voxpopuli_nl | 2023-07-19T10:58:48.000Z | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | text-to-audio | susnato | null | null | susnato/speecht5_finetuned_voxpopuli_nl | 0 | 2 | transformers | 2023-07-19T08:42:38 | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5263 | 4.3 | 1000 | 0.4794 |
| 0.5015 | 8.6 | 2000 | 0.4671 |
| 0.4927 | 12.9 | 3000 | 0.4624 |
| 0.4854 | 17.2 | 4000 | 0.4608 |
### Framework versions
- Transformers 4.32.0.dev0
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.2 | 1,599 | [
[
-0.0322265625,
-0.04022216796875,
-0.005123138427734375,
0.00908660888671875,
-0.0195770263671875,
-0.0242919921875,
-0.01444244384765625,
-0.00939178466796875,
-0.00875091552734375,
0.020843505859375,
-0.047943115234375,
-0.050628662109375,
-0.042999267578125,
... |
rafaym/LoRaModel | 2023-07-21T13:27:17.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | rafaym | null | null | rafaym/LoRaModel | 0 | 2 | diffusers | 2023-07-19T10:49:31 |
---
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 - rafaym/LoRaModel
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the rafaym/meri_tasweer dataset. You can find some example images in the following.
| 524 | [
[
-0.01947021484375,
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0.0077667236328125,
0.0270538330078125,
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0.0241851806640625,
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0.021240234375,
0.060760498046875,
-0.060455322265625,
-0.041534423828125,
-0.047637939453125,
-0... |
zaidazhari/ppo-LunarLander-v2 | 2023-07-19T10:56:38.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | zaidazhari | null | null | zaidazhari/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T10:56:17 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.19 +/- 8.90
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
...
```
| 783 | [
[
-0.00020432472229003906,
-0.027130126953125,
0.0170745849609375,
0.0233612060546875,
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0.0027675628662109375,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125... |
zampoan/ppo-LunarLander-v2 | 2023-07-19T12:33:54.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | zampoan | null | null | zampoan/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T12:33:01 | ---
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: 168.10 +/- 17.44
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
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-0.035247802734375,
-0.0343017578125,
-... |
bspies/ppo-LunarLander-v2 | 2023-07-19T13:46:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | bspies | null | null | bspies/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T13:46:19 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.54 +/- 20.81
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
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0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
StKirill/ppo-LunarLander-v2 | 2023-07-19T13:48:30.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | StKirill | null | null | StKirill/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T13:48:12 | ---
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: 247.47 +/- 68.18
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
...
```
| 784 | [
[
-0.0001958608627319336,
-0.02716064453125,
0.0170745849609375,
0.0233306884765625,
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0.034423828125,
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0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,
... |
MatteoColavita/ppo-LunarLander-v2 | 2023-07-19T13:50:33.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | MatteoColavita | null | null | MatteoColavita/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T13:50:10 | ---
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: 259.71 +/- 16.68
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.0001958608627319336,
-0.02716064453125,
0.0170745849609375,
0.0233306884765625,
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0.034423828125,
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0.019866943359375,
0.06500244140625,
-0.043182373046875,
-0.035247802734375,
-0.0343017578125,
... |
giocs2017/a2c-AntBulletEnv-v0 | 2023-07-19T14:07:55.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | giocs2017 | null | null | giocs2017/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-19T14:06:49 | ---
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: 1658.96 +/- 224.56
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
...
```
| 791 | [
[
-0.0267791748046875,
-0.044403076171875,
0.01070404052734375,
0.0208740234375,
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0.0187530517578125,
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0.0193939208984375,
0.026580810546875,
-0.052581787109375,
-0.037506103515625,
-0.04425048828125,
... |
robertpassmann/ppo-LunarLander-v2 | 2023-07-19T14:15:21.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | robertpassmann | null | null | robertpassmann/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T14:14:58 | ---
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 +/- 11.37
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
-0.00606536865234375,
0.002735137939453125,
0.034454345703125,
-0.012115478515625,
0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
giocs2017/a2c-PandaReachDense-v2 | 2023-07-19T14:58:29.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | giocs2017 | null | null | giocs2017/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T14:55:46 | ---
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: -2.35 +/- 0.47
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
...
```
| 802 | [
[
-0.019744873046875,
-0.04742431640625,
-0.004787445068359375,
0.0469970703125,
-0.00018846988677978516,
-0.006023406982421875,
0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
dnarqq/ppo-LunarLander-v2 | 2023-07-19T17:44:31.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | dnarqq | null | null | dnarqq/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T15:45:27 | ---
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: 283.73 +/- 19.72
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
...
```
| 784 | [
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0.06500244140625,
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-... |
albagon/ppo-LunarLander-v2 | 2023-07-19T15:51:23.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | albagon | null | null | albagon/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T15:51:05 | ---
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: 257.03 +/- 19.86
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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0.06500244140625,
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-... |
germla/satoken | 2023-09-01T03:55:12.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"en",
"fr",
"ko",
"zh",
"ja",
"pt",
"ru",
"dataset:imdb",
"doi:10.57967/hf/0905",
"license:apache-2.0",
"model-index",
"region:us"
] | text-classification | germla | null | null | germla/satoken | 1 | 2 | sentence-transformers | 2023-07-19T16:09:11 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
- accuracy
- f1
- precision
- recall
language:
- en
- fr
- ko
- zh
- ja
- pt
- ru
datasets:
- imdb
model-index:
- name: germla/satoken
results:
- task:
type: text-classification
name: sentiment-analysis
dataset:
type: imdb
name: imdb
split: test
metrics:
- type: accuracy
value: 73.976
name: Accuracy
- type: f1
value: 73.1667079105832
name: F1
- type: precision
value: 75.51506895964584
name: Precision
- type: recall
value: 70.96
name: Recall
- task:
type: text-classification
name: sentiment-analysis
dataset:
type: sepidmnorozy/Russian_sentiment
name: sepidmnorozy/Russian_sentiment
split: train
metrics:
- type: accuracy
value: 75.66371681415929
name: Accuracy
- type: f1
value: 83.64218714253031
name: F1
- type: precision
value: 75.25730753396459
name: Precision
- type: recall
value: 94.129763130793
name: Recall
---
# Satoken
This is a [SetFit model](https://github.com/huggingface/setfit) trained on multilingual datasets (mentioned below) for Sentiment classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
It is utilized by [Germla](https://github.com/germla) for it's feedback analysis tool. (specifically the Sentiment analysis feature)
For other models (specific language-basis) check [here](https://github.com/germla/satoken#available-models)
# Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("germla/satoken")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
# Training Details
## Training Data
- [IMDB](https://huggingface.co/datasets/imdb)
- [RuReviews](https://github.com/sismetanin/rureviews)
- [chABSA](https://github.com/chakki-works/chABSA-dataset)
- [Glyph](https://github.com/zhangxiangxiao/glyph)
- [nsmc](https://github.com/e9t/nsmc)
- [Allocine](https://huggingface.co/datasets/allocine)
- [Portuguese Tweets for Sentiment Analysis](https://www.kaggle.com/datasets/augustop/portuguese-tweets-for-sentiment-analysis)
## Training Procedure
We made sure to have a balanced dataset.
The model was trained on only 35% (50% for chinese) of the train split of all datasets.
### Preprocessing
- Basic Cleaning (removal of dups, links, mentions, hashtags, etc.)
- Removal of stopwords using [nltk](https://www.nltk.org/)
### Speeds, Sizes, Times
The training procedure took 6hours on the NVIDIA T4 GPU.
## Evaluation
### Testing Data, Factors & Metrics
- [IMDB test split](https://huggingface.co/datasets/imdb)
# Environmental Impact
- Hardware Type: NVIDIA T4 GPU
- Hours used: 6
- Cloud Provider: Amazon Web Services
- Compute Region: ap-south-1 (Mumbai)
- Carbon Emitted: 0.39 [kg co2 eq.](https://mlco2.github.io/impact/#co2eq)
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0.01... |
Junr-syl/tweet_sentiments_analysis_distilbert-base-uncased | 2023-07-19T17:12:04.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Junr-syl | null | null | Junr-syl/tweet_sentiments_analysis_distilbert-base-uncased | 0 | 2 | transformers | 2023-07-19T16:21:34 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: tweet_sentiments_analysis_distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tweet_sentiments_analysis_distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1715
- F1: 0.7180
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7542 | 1.0 | 1000 | 0.7429 | 0.6630 |
| 0.7264 | 2.0 | 2000 | 0.7214 | 0.6782 |
| 0.6376 | 3.0 | 3000 | 0.6610 | 0.7171 |
| 0.5196 | 4.0 | 4000 | 0.7578 | 0.7291 |
| 0.4344 | 5.0 | 5000 | 0.8670 | 0.7248 |
| 0.3342 | 6.0 | 6000 | 1.0522 | 0.7223 |
| 0.2841 | 7.0 | 7000 | 1.1715 | 0.7180 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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piotrsuder/ppo-LunarLander-v2 | 2023-07-19T16:22:04.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | piotrsuder | null | null | piotrsuder/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T16:21:36 | ---
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: 178.94 +/- 67.66
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
...
```
| 784 | [
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-... |
chandan9t8/a2c-AntBulletEnv-v0 | 2023-07-19T16:45:56.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | chandan9t8 | null | null | chandan9t8/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-19T16:44:48 | ---
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: 1571.33 +/- 34.35
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
...
```
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Trelis/mpt-7b-8k-chat-sharded-bf16 | 2023-07-25T16:34:38.000Z | [
"transformers",
"pytorch",
"mpt",
"text-generation",
"Composer",
"MosaicML",
"llm-foundry",
"sharded",
"custom_code",
"dataset:camel-ai/code",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"dataset:anon8231489123/ShareGPT_Vicuna_unfiltered",
"dataset:teknium1/GPTeacher/roleplay-instruct-... | text-generation | Trelis | null | null | Trelis/mpt-7b-8k-chat-sharded-bf16 | 1 | 2 | transformers | 2023-07-19T18:09:15 | ---
license: cc-by-nc-sa-4.0
datasets:
- camel-ai/code
- ehartford/wizard_vicuna_70k_unfiltered
- anon8231489123/ShareGPT_Vicuna_unfiltered
- teknium1/GPTeacher/roleplay-instruct-v2-final
- teknium1/GPTeacher/codegen-isntruct
- timdettmers/openassistant-guanaco
- camel-ai/math
- project-baize/baize-chatbot/medical_chat_data
- project-baize/baize-chatbot/quora_chat_data
- project-baize/baize-chatbot/stackoverflow_chat_data
- camel-ai/biology
- camel-ai/chemistry
- camel-ai/ai_society
- jondurbin/airoboros-gpt4-1.2
- LongConversations
- camel-ai/physics
tags:
- Composer
- MosaicML
- llm-foundry
- sharded
- mpt
inference: false
---
# MPT-7B-Chat-8k-sharded-bf16
## Sharded version of mpt-7b-8k-chat from MosaicML
Sharded using a Google Colab notebook: https://colab.research.google.com/drive/1f1q9qc56wzB_7-bjgNyLlO6f28ui1esQ?usp=sharing
Information below is copy-pasted from MosaicML.
# MPT-7B-Chat-8k
MPT-7B-Chat-8k is a chatbot-like model for dialogue generation.
It was built by finetuning [MPT-7B-8k](https://huggingface.co/mosaicml/mpt-7b-8k) on the [ShareGPT-Vicuna](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered), [Camel-AI](https://huggingface.co/camel-ai),
[GPTeacher](https://github.com/teknium1/GPTeacher), [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Baize](https://github.com/project-baize/baize-chatbot) and some generated datasets.
This is the same dataset that [MPT-30B-Chat](https://huggingface.co/mosaicml/mpt-30b-chat) was trained on.
* License: _CC-By-NC-SA-4.0_ (non-commercial use only)
This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.
## Model Date
July 18, 2023
## Model License
_CC-By-NC-SA-4.0_ (non-commercial use only)
## Documentation
* [Blog post: MPT-7B-8k](https://www.mosaicml.com/blog/long-context-mpt-7b-8k)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!
## How to Use
This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-chat-8k',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision:
```python
import torch
import transformers
name = 'mosaicml/mpt-7b-chat-8k'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention
config.init_device = 'cuda:0' # For fast initialization directly on GPU!
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
torch_dtype=torch.bfloat16, # Load model weights in bfloat16
trust_remote_code=True
)
```
The model was trained initially with a sequence length of 2048 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example:
```python
import transformers
name = 'mosaicml/mpt-7b-chat-8k'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
trust_remote_code=True
)
```
This model was trained with the MPT-7B-chat tokenizer which is based on the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer and includes additional ChatML tokens.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-7b-8k')
```
The model can then be used, for example, within a text-generation pipeline.
Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html).
```python
from transformers import pipeline
with torch.autocast('cuda', dtype=torch.bfloat16):
inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda')
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# or using the HF pipeline
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(
pipe('Here is a recipe for vegan banana bread:\n',
max_new_tokens=100,
do_sample=True,
use_cache=True))
```
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 6.7B |
|n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 2048 |
## Data Mix
The model was trained on the following data mix:
| Data Source | Number of Tokens in Source | Proportion |
|-------------|----------------------------|------------|
| Airoboros/GPT4-1.2 | 26.4M | 1.71% |
| Baize | 55.0M | 3.57% |
| Camel | 301M | 19.54% |
| GPTeacher | 7.56M | 0.49% |
| Guanaco | 15.6M | 1.02% |
| LongCoversations | 18.4M | 1.19% |
| ShareGPT | 821M | 53.24% |
| WizardLM | 297M | 19.23% |
"LongConversations" is a GPT3.5/4-generated dataset, details of which will be released at a later date.
### Training Configuration
This model was trained on 192 H100s for about 48 minutes using the [MosaicML Platform](https://www.mosaicml.com/platform).
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-7B-Chat-8k can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-7B-Chat-8k was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## Acknowledgements
This model was finetuned by the MosaicML NLP team
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://www.mosaicml.com/get-started?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b-8k).
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-30B: Raising the bar
for open-source foundation models},
year = {2023},
url = {www.mosaicml.com/blog/mpt-30b},
note = {Accessed: 2023-06-22},
urldate = {2023-06-22}
}
``` | 8,232 | [
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Darisian/ppo-LunarLander-custom | 2023-07-20T13:36:22.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Darisian | null | null | Darisian/ppo-LunarLander-custom | 0 | 2 | stable-baselines3 | 2023-07-19T18:31:43 | ---
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: 95.89 +/- 105.68
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
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0.023345947265625,
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0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
PlankyxD/ppo-Pyramids | 2023-07-19T18:37:16.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | PlankyxD | null | null | PlankyxD/ppo-Pyramids | 0 | 2 | ml-agents | 2023-07-19T18:37:11 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: PlankyxD/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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0.017364501953125,
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0.033721923828125,
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Jelliott/ppo-LunarLander-v2 | 2023-07-19T19:37:20.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Jelliott | null | null | Jelliott/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T19:23:13 | ---
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: 271.52 +/- 19.67
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
...
```
| 784 | [
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0.034454345703125,
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0.06500244140625,
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-... |
akdeniz27/ppo-Pyramids | 2023-07-19T22:18:47.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | akdeniz27 | null | null | akdeniz27/ppo-Pyramids | 0 | 2 | ml-agents | 2023-07-19T22:18:40 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: akdeniz27/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,334 | [
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0.0168609619140625,
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0.033233642578125,
0.0300140380859375,
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ByteExplorer/a2c-PandaReachDense-v2 | 2023-07-21T00:04:32.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | ByteExplorer | null | null | ByteExplorer/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-19T22:40:05 | ---
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: -2.01 +/- 0.22
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
...
```
| 802 | [
[
-0.019744873046875,
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0.0469970703125,
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0.033172607421875,
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0.028045654296875,
0.042694091796875,
-0.06256103515625,
-0.0289764404296875,
-0.03277587890625... |
snicolau/ppo-Pyramids | 2023-07-19T23:45:23.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | snicolau | null | null | snicolau/ppo-Pyramids | 0 | 2 | ml-agents | 2023-07-19T23:44:31 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: snicolau/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| 1,333 | [
[
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0.01409912109375,
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0.01629638671875,
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0.034332275390625,
0.0302581787109375,
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-0.04986572265625,
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-0.01... |
PlankyxD/a2c-AntBulletEnv-v0 | 2023-07-20T00:05:24.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | PlankyxD | null | null | PlankyxD/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-20T00:04:08 | ---
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: 1098.32 +/- 98.86
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
...
```
| 790 | [
[
-0.0267791748046875,
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0.0208892822265625,
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0.0187530517578125,
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brunoboat/ppo-LunarLander-v2 | 2023-07-20T00:43:14.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | brunoboat | null | null | brunoboat/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T00:42:56 | ---
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: 269.80 +/- 21.33
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
...
```
| 784 | [
[
-0.00023484230041503906,
-0.02716064453125,
0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
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-0.035247802734375,
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-... |
marianafmedeiros/a2c-AntBulletEnv-v0 | 2023-07-20T12:20:04.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | marianafmedeiros | null | null | marianafmedeiros/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-20T03:03:24 | ---
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: 846.46 +/- 66.62
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
...
```
| 789 | [
[
-0.02679443359375,
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0.0187530517578125,
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0.0193939208984375,
0.0265655517578125,
-0.052642822265625,
-0.037506103515625,
-0.04425048828125,
... |
kaikaikaikaikaikaikaikai/marian-finetuned-kftt-ja-to-en | 2023-07-27T08:28:04.000Z | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kftt",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | kaikaikaikaikaikaikaikai | null | null | kaikaikaikaikaikaikaikai/marian-finetuned-kftt-ja-to-en | 0 | 2 | transformers | 2023-07-20T03:04:20 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kftt
metrics:
- bleu
model-index:
- name: marian-finetuned-kftt-ja-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kftt
type: kftt
config: en-ja
split: validation
args: en-ja
metrics:
- name: Bleu
type: bleu
value: 19.353560365370512
---
<!-- 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. -->
# marian-finetuned-kftt-ja-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on the kftt dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9124
- Bleu: 19.3536
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.3.2
- Tokenizers 0.13.3
| 1,525 | [
[
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0.0... |
aroot/eng-kor-delfy | 2023-07-24T02:37:57.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | aroot | null | null | aroot/eng-kor-delfy | 0 | 2 | transformers | 2023-07-20T03:30:37 | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-kor-delfy
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. -->
# eng-kor-delfy
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0666
- Bleu: 5.9354
- Chrf: 23.5962
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0
| 1,174 | [
[
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0.018310546875,
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teilomillet/ppo-LunarLander-v2 | 2023-07-25T21:15:19.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | teilomillet | null | null | teilomillet/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T04:00:54 | ---
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: 292.03 +/- 14.79
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| 784 | [
[
-0.00023484230041503906,
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0.017059326171875,
0.023345947265625,
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0.002735137939453125,
0.034454345703125,
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0.019866943359375,
0.06500244140625,
-0.043212890625,
-0.035247802734375,
-0.0343017578125,
-... |
kkmkorea/checkpoint25000 | 2023-08-07T00:25:12.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"ko",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | kkmkorea | null | null | kkmkorea/checkpoint25000 | 0 | 2 | transformers | 2023-07-20T05:13:54 | ---
license: mit
language:
- ko
metrics:
- f1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | 5,211 | [
[
-0.04803466796875,
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shanover/disease_classifier_base | 2023-07-20T08:58:08.000Z | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"bert-base-uncased",
"disease",
"medical",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | shanover | null | null | shanover/disease_classifier_base | 0 | 2 | transformers | 2023-07-20T05:32:16 | ---
license: mit
language:
- en
library_name: transformers
pipeline_tag: text-classification
tags:
- bert-base-uncased
- disease
- medical
widget:
- text: "I am having itching, skin rash, and nodal skin eruptions"
example_title: "Fungal infection example"
- text: "I feel like vomiting, breathlessness, and sweating"
example_title: "Heart Attack example"
- text: "I am feeling fatigue, weight loss, restlessness and also lethargy."
example_title: "Diabetes example"
---
The objective is to develop a symptom-to-disease classification model for a natural language chatbot. This model takes input text such as "I am feeling vomiting, breathlessness, and sweating" and accurately identifies the associated disease (2 - 'Heart attack').
In essence, the chatbot's purpose is to analyze users' symptoms and provide relevant disease predictions in real-time conversation.
Labels:
0 - Fungal infection
1 - Diabetes
2 - Heart attack
Will add more diseases in coming days | 987 | [
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0... |
Claaas/ppo-LunarLander-v2 | 2023-07-20T06:11:20.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Claaas | null | null | Claaas/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T06:11:05 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.69 +/- 21.30
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
...
```
| 784 | [
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Claaas/ppo-Huggy | 2023-07-20T06:34:45.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | Claaas | null | null | Claaas/ppo-Huggy | 0 | 2 | ml-agents | 2023-07-20T06:34:43 | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Claaas/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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lianlian123/ppo-LunarLander-v2 | 2023-07-20T07:50:25.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | lianlian123 | null | null | lianlian123/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T07:50:04 | ---
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: 250.36 +/- 13.46
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
...
```
| 784 | [
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-... |
ankush-003/bart-nosqli | 2023-07-24T07:40:27.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | ankush-003 | null | null | ankush-003/bart-nosqli | 0 | 2 | transformers | 2023-07-20T10:04:16 | ---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
model-index:
- name: bart-nosqli
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. -->
# bart-nosqli
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
| 1,047 | [
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bwilkie/a2c-PandaReachDense-v2 | 2023-07-20T16:35:18.000Z | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | bwilkie | null | null | bwilkie/a2c-PandaReachDense-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T10:51:29 | ---
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: -5.73 +/- 1.19
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
...
```
| 802 | [
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budecosystem/genz-13b | 2023-07-20T15:28:37.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | budecosystem | null | null | budecosystem/genz-13b | 2 | 2 | transformers | 2023-07-20T11:15:54 | ---
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# GenZ 13B
The instruction finetuned model with 4K input length. The model is finetuned on top of pretrained LLaMa2
## Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("budecosystem/genz-13b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-13b", torch_dtype=torch.bfloat16)
inputs = tokenizer("The world is", return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))
```
Use following prompt template
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi, how are you? ASSISTANT:
```
## Finetuning
```bash
python finetune.py
--model_name meta-llama/Llama-2-13b
--data_path dataset.json
--output_dir output
--trust_remote_code
--prompt_column instruction
--response_column output
```
Check the GitHub for the code -> [GenZ](https://github.com/BudEcosystem/GenZ) | 1,160 | [
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jaygdesai/jay-default-ppo-LunarLander-v2 | 2023-07-20T20:33:01.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jaygdesai | null | null | jaygdesai/jay-default-ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-07-20T11:17:18 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.06 +/- 22.80
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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giovannidispoto/a2c-AntBulletEnv-v0 | 2023-07-20T13:25:13.000Z | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | giovannidispoto | null | null | giovannidispoto/a2c-AntBulletEnv-v0 | 0 | 2 | stable-baselines3 | 2023-07-20T13:24:09 | ---
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: 2081.25 +/- 50.17
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
...
```
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patebel/LunarLander | 2023-07-20T14:25:11.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | patebel | null | null | patebel/LunarLander | 0 | 2 | stable-baselines3 | 2023-07-20T13:58:32 | ---
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: -70.75 +/- 91.73
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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mstaron/wolfBERTa | 2023-07-20T15:14:17.000Z | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | mstaron | null | null | mstaron/wolfBERTa | 0 | 2 | transformers | 2023-07-20T14:54:57 | ---
license: mit
---
This model is a RoBERTa model trained on a programming language code - WolfSSL.
The programming language is C/C++, but the actual inference can also use other languages.
Using the model to unmask can be done in the following way
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='mstaron/wolfBERTa')
unmasker("Hello I'm a <mask> model.")
```
To obtain the embeddings for downstream task can be done in the following way:
```python
# import the model via the huggingface library
from transformers import AutoTokenizer, AutoModelForMaskedLM
# load the tokenizer and the model for the pretrained wolfBERTa
tokenizer = AutoTokenizer.from_pretrained('mstaron/wolfBERTa')
# load the model
model = AutoModelForMaskedLM.from_pretrained("mstaron/wolfBERTa")
# import the feature extraction pipeline
from transformers import pipeline
# create the pipeline, which will extract the embedding vectors
# the models are already pre-defined, so we do not need to train anything here
features = pipeline(
"feature-extraction",
model=model,
tokenizer=tokenizer,
return_tensor = False
)
# extract the features == embeddings
lstFeatures = features('Class HTTP::X1')
# print the first token's embedding [CLS]
# which is also a good approximation of the whole sentence embedding
# the same as using np.mean(lstFeatures[0], axis=0)
lstFeatures[0][0]
```
In order to use the model, we need to train it on the downstream task.
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hyungtae/mpt-30b | 2023-07-21T00:47:36.000Z | [
"transformers",
"pytorch",
"mpt",
"text-generation",
"Composer",
"MosaicML",
"llm-foundry",
"StreamingDatasets",
"custom_code",
"dataset:allenai/c4",
"dataset:mc4",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/the-stack-dedup",
"dataset:allenai/s2orc",
"arxiv:2108.12409... | text-generation | hyungtae | null | null | hyungtae/mpt-30b | 0 | 2 | transformers | 2023-07-20T16:31:38 | ---
license: apache-2.0
tags:
- Composer
- MosaicML
- llm-foundry
- StreamingDatasets
datasets:
- allenai/c4
- mc4
- togethercomputer/RedPajama-Data-1T
- bigcode/the-stack-dedup
- allenai/s2orc
inference: false
---
### Attribution
This model is derived from [MosaicML's MPT-30B model](https://huggingface.co/mosaicml/mpt-30b/tree/main), with changes from
[cekal/mpt-7b-peft-compatible](https://huggingface.co/cekal/mpt-7b-peft-compatible) applied; each licensed under the
Apache License, version 2.0.
# MPT-30B
MPT-30B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code.
This model was trained by [MosaicML](https://www.mosaicml.com).
MPT-30B is part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.
MPT-30B comes with special features that differentiate it from other LLMs, including an 8k token context window (which can be further extended via finetuning; see [MPT-7B-StoryWriter](https://huggingface.co/mosaicml/mpt-7b-storywriter)), support for context-length extrapolation via [ALiBi](https://arxiv.org/abs/2108.12409), and efficient inference + training via FlashAttention. It also has strong coding abilities thanks to its pretraining mix. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer).
The size of MPT-30B was also specifically chosen to make it easy to deploy on a single GPU—either 1xA100-80GB in 16-bit precision or 1xA100-40GB in 8-bit precision.
This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference.
### How is this model different?
MPT-30B is:
* **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)).
* **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)).
* **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409).
* **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer))
* **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry)
### Models finetuned off MPT-30B:
The following models are finetuned on MPT-30B:
* [MPT-30B-Instruct](https://huggingface.co/mosaicml/mpt-30b-instruct): a model for long-form instruction following (especially summarization and question-answering).
Built by finetuning MPT-30B on several carefully curated datasets.
* License: _CC-BY-SA-3.0_
* [MPT-30B-Chat](https://huggingface.co/mosaicml/mpt-30b-chat): a chatbot-like model for dialogue generation.
Built by finetuning MPT-30B on [ShareGPT-Vicuna](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered), [Camel-AI](https://huggingface.co/camel-ai),
[GPTeacher](https://github.com/teknium1/GPTeacher), [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), [Baize](https://github.com/project-baize/baize-chatbot) and some generated datasets.
* License: _CC-By-NC-SA-4.0_
* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-30b-chat)
## Model Date
June 22, 2023
## Model License
Apache-2.0
## Documentation
* [Blog post: MPT-30B: Raising the bar for open-source foundation models](https://www.mosaicml.com/blog/mpt-30b)
* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!
## How to Use
This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning.
```python
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-30b',
trust_remote_code=True
)
```
Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more.
To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision:
```python
import torch
import transformers
name = 'mosaicml/mpt-30b'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton' # change this to use triton-based FlashAttention
config.init_device = 'cuda:0' # For fast initialization directly on GPU!
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
torch_dtype=torch.bfloat16, # Load model weights in bfloat16
trust_remote_code=True
)
```
The model was trained initially with a sequence length of 2048 with an additional pretraining stage for sequence length adapation up to 8192. However, ALiBi enables users to increase the maximum sequence length even further during finetuning and/or inference. For example:
```python
import transformers
name = 'mosaicml/mpt-30b'
config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 16384 # (input + output) tokens can now be up to 16384
model = transformers.AutoModelForCausalLM.from_pretrained(
name,
config=config,
trust_remote_code=True
)
```
This model was trained with the MPT-30B tokenizer which is identical to the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b')
```
The model can then be used, for example, within a text-generation pipeline.
Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html).
```python
from transformers import pipeline
with torch.autocast('cuda', dtype=torch.bfloat16):
inputs = tokenizer('Here is a recipe for vegan banana bread:\n', return_tensors="pt").to('cuda')
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# or using the HF pipeline
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(
pipe('Here is a recipe for vegan banana bread:\n',
max_new_tokens=100,
do_sample=True,
use_cache=True))
```
## Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
* It does not use biases
| Hyperparameter | Value |
|----------------|-------|
|n_parameters | 29.95B |
|n_layers | 48 |
| n_heads | 64 |
| d_model | 7168 |
| vocab size | 50432 |
| sequence length | 8192 |
## Training Data
### Streaming Datasets
Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training.
StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset.
### Data Mix
The model was trained for 1T tokens on the following data mix:
| Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs |
|-------------|----------------------------|------------|----------------------------|--------|
| mC4 3.1.0 - English (200+ words) | 2417.99 B | 33.50% | 335 B | 0.14 |
| c4 - English - SemDedup 80% | 100.42 B | 29.90% | 299 B | 2.98 |
| RedPajama - CommonCrawl | 878.45 B | 8.50% | 85 B | 0.097 |
| The Stack - Selected Languages | 463.78 B | 10.00% | 100 B | 0.22 |
| RedPajama - Wikipedia | 4.87 B | 4.00% | 40 B | 8.21 |
| The Stack - Markdown | 107.07 B | 4.50% | 45 B | 0.42 |
| Semantic Scholar ORC | 48.95 B | 3.30% | 33 B | 0.67 |
| RedPajama - Books | 26.02 B | 3.00% | 30 B | 1.15 |
| RedPajama - arXiv | 28.10 B | 1.90% | 19 B | 0.68 |
| RedPajama - StackExchange | 20.54 B | 1.40% | 14 B |0.68 |
Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the sequence length. To build 8k support into MPT-30B efficiently, we first pre-trained on 1T tokens using sequences that were 2k tokens long, and then trained for an additional 50B tokens using sequences that were 8k tokens long.
The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics,
most of which are relevant for tokenizing code:
(1) It was trained on a diverse mix of data that includes code (The Pile)
(2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces
(3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters.
The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)).
### Training Configuration
The model was trained in three stages using the [MosaicML Platform](https://www.mosaicml.com/platform):
(i) First it was trained on 440 A100-40GBs with a batch size of 1760.
(ii) Then, on 216 A100-40GBs with a batch size of 1728.
(iii) Training was completed on 256 H100-80GBs with a batch size of 512 with 8k context length and 50B tokens.
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer.
## Limitations and Biases
_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
MPT-30B (Base) is **not** intended for deployment without finetuning.
It should not be used for human-facing interactions without further guardrails and user consent.
MPT-30B can produce factually incorrect output, and should not be relied on to produce factually accurate information.
MPT-30B was trained on various public datasets.
While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
## MosaicML Platform
If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-30b).
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
## Citation
Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-30B: Raising the bar
for open-source foundation models},
year = {2023},
url = {www.mosaicml.com/blog/mpt-30b},
note = {Accessed: 2023-06-22},
urldate = {2023-06-22}
}
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