modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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Closen/Pixelcopter-PLE-v0_PG
|
Closen
| 2023-02-02T09:29:35Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-02T09:21:32Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0_PG
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.80 +/- 26.48
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jojoUla/bert-large-cased-finetuned-low20-cased-DA-20
|
jojoUla
| 2023-02-02T09:05:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-02-02T08:34:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-finetuned-low20-cased-DA-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-low20-cased-DA-20 (not in use)
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3667
## 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.477 | 1.0 | 1 | 3.0843 |
| 3.5516 | 2.0 | 2 | 4.2279 |
| 3.6173 | 3.0 | 3 | 4.2543 |
| 3.1873 | 4.0 | 4 | 2.8752 |
| 3.9494 | 5.0 | 5 | 1.7727 |
| 2.628 | 6.0 | 6 | 2.2849 |
| 1.7451 | 7.0 | 7 | 2.2338 |
| 2.6641 | 8.0 | 8 | 1.4185 |
| 3.0739 | 9.0 | 9 | 4.0617 |
| 2.1557 | 10.0 | 10 | 3.4256 |
| 1.6353 | 11.0 | 11 | 3.0232 |
| 2.6313 | 12.0 | 12 | 4.2908 |
| 1.9466 | 13.0 | 13 | 3.0047 |
| 1.8104 | 14.0 | 14 | 2.9170 |
| 2.0315 | 15.0 | 15 | 3.5850 |
| 2.6848 | 16.0 | 16 | 4.4435 |
| 2.0859 | 17.0 | 17 | 3.9439 |
| 1.6852 | 18.0 | 18 | 0.9313 |
| 1.6071 | 19.0 | 19 | 3.6927 |
| 1.697 | 20.0 | 20 | 3.7250 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_mrpc_128
|
gokuls
| 2023-02-02T09:00:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-02T00:15:14Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_mrpc_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_mrpc_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- 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 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.2019 | 1.0 | 1959 | 0.0211 | 0.9926 | 0.9947 | 0.9936 |
| 0.0464 | 2.0 | 3918 | 0.0122 | 0.9951 | 0.9964 | 0.9958 |
| 0.0307 | 3.0 | 5877 | 0.0049 | 0.9975 | 0.9982 | 0.9979 |
| 0.0223 | 4.0 | 7836 | 0.0041 | 0.9975 | 0.9982 | 0.9979 |
| 0.0179 | 5.0 | 9795 | 0.0006 | 1.0 | 1.0 | 1.0 |
| 0.0147 | 6.0 | 11754 | 0.0005 | 1.0 | 1.0 | 1.0 |
| 0.012 | 7.0 | 13713 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0086 | 8.0 | 15672 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0064 | 9.0 | 17631 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0058 | 10.0 | 19590 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0043 | 11.0 | 21549 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0035 | 12.0 | 23508 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.003 | 13.0 | 25467 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0024 | 14.0 | 27426 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 15.0 | 29385 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 16.0 | 31344 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 17.0 | 33303 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 18.0 | 35262 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.001 | 19.0 | 37221 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 20.0 | 39180 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 21.0 | 41139 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 22.0 | 43098 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 23.0 | 45057 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 24.0 | 47016 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 25.0 | 48975 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 26.0 | 50934 | 0.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Danghor/NLP4Web
|
Danghor
| 2023-02-02T08:46:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-01T17:53:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: result
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. -->
# result
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
paigereeves/distilbert-base-uncased-finetuned-auto
|
paigereeves
| 2023-02-02T08:43:12Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-01-31T04:49:19Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: paigereeves/distilbert-base-uncased-finetuned-auto
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# paigereeves/distilbert-base-uncased-finetuned-auto
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.7987
- Validation Loss: 3.4097
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.7987 | 3.4097 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
MHaurel/a2c-AntBulletEnv-v0
|
MHaurel
| 2023-02-02T08:38:29Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-02T08:37:23Z |
---
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: 1812.08 +/- 54.55
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
...
```
|
scronberg/poca-SoccerTwos
|
scronberg
| 2023-02-02T08:24:17Z | 62 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-02-02T08:24:09Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: scronberg/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
nolanaatama/asslora
|
nolanaatama
| 2023-02-02T08:15:15Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-02T08:12:45Z |
---
license: creativeml-openrail-m
---
|
Addwater/a2c-AntBulletEnv-v0
|
Addwater
| 2023-02-02T08:04:30Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-02T08:03:26Z |
---
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: 1693.64 +/- 82.00
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
...
```
|
zhenligod/videomae-base-finetuned-ucf101-subset
|
zhenligod
| 2023-02-02T08:02:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-02-02T03:22:20Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset
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. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2120
- Accuracy: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.5 | 1 | 1.2203 | 0.0 |
| No log | 1.5 | 2 | 1.0272 | 0.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
sd-concepts-library/ahx-model-10
|
sd-concepts-library
| 2023-02-02T07:52:41Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-02-02T07:52:38Z |
---
license: mit
---
### ahx-model-10 on Stable Diffusion
This is the `<ahx-model-10>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
ykleeee/wav2vec2-5epochs-3e4
|
ykleeee
| 2023-02-02T07:50:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-01T08:21:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-owndata
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-owndata
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2515
- Wer: 0.3212
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.262 | 0.36 | 100 | 3.4482 | 0.9832 |
| 3.0032 | 0.72 | 200 | 2.9441 | 0.9832 |
| 2.9141 | 1.08 | 300 | 2.9393 | 0.9832 |
| 2.8585 | 1.44 | 400 | 2.8848 | 0.9627 |
| 2.2837 | 1.8 | 500 | 2.1732 | 1.0111 |
| 0.9834 | 2.16 | 600 | 0.8765 | 0.7345 |
| 0.7288 | 2.52 | 700 | 0.5741 | 0.5641 |
| 0.5521 | 2.88 | 800 | 0.3937 | 0.4467 |
| 0.3751 | 3.24 | 900 | 0.3484 | 0.4112 |
| 0.3733 | 3.6 | 1000 | 0.2964 | 0.3912 |
| 0.2443 | 3.96 | 1100 | 0.2673 | 0.3446 |
| 0.2667 | 4.32 | 1200 | 0.2657 | 0.3357 |
| 0.2237 | 4.68 | 1300 | 0.2515 | 0.3212 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 2.9.0
- Tokenizers 0.10.3
|
FoxFive/LunarLander-v2-ppo-2_1
|
FoxFive
| 2023-02-02T07:42:59Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-02-02T07:42:59Z |
---
license: bigscience-bloom-rail-1.0
---
|
amrisaurus/pretrained-bert-uncased-200
|
amrisaurus
| 2023-02-02T07:26:41Z | 1 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2023-02-01T16:40:52Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-bert-uncased-200
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pretrained-bert-uncased-200
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- Epoch: 199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.9076 | 9.5544 | 0 |
| 7.0572 | 9.6310 | 1 |
| 6.5781 | 10.4973 | 2 |
| 6.1054 | 10.4749 | 3 |
| 6.1980 | 10.4411 | 4 |
| 6.0896 | 11.1385 | 5 |
| 6.1630 | 10.8668 | 6 |
| 5.9313 | 11.2520 | 7 |
| 5.7459 | 10.9396 | 8 |
| 5.8505 | 11.1343 | 9 |
| 5.8592 | 11.6048 | 10 |
| 5.7595 | 12.0371 | 11 |
| 5.7283 | 11.4402 | 12 |
| 5.7948 | 11.6117 | 13 |
| 5.7973 | 11.7393 | 14 |
| 5.6228 | 11.9450 | 15 |
| 5.6996 | 11.9938 | 16 |
| 5.7468 | 12.3826 | 17 |
| 5.6336 | 11.7692 | 18 |
| 5.6287 | 12.1970 | 19 |
| 5.7435 | 12.3895 | 20 |
| 5.6587 | 12.2124 | 21 |
| 5.6767 | 12.1633 | 22 |
| 5.7494 | 12.1844 | 23 |
| 5.5532 | 12.4163 | 24 |
| 5.4826 | 12.3235 | 25 |
| 5.7103 | 12.7326 | 26 |
| 5.6399 | 12.3326 | 27 |
| 5.6171 | 12.4726 | 28 |
| 5.8517 | 12.3647 | 29 |
| 5.6446 | 12.4943 | 30 |
| 5.5662 | 12.6303 | 31 |
| 5.8222 | 12.5869 | 32 |
| 5.5710 | 13.0406 | 33 |
| 5.6011 | 12.5007 | 34 |
| 5.6860 | 12.2958 | 35 |
| 5.6071 | 12.5690 | 36 |
| 5.5824 | 12.4472 | 37 |
| 5.5800 | 12.8570 | 38 |
| 5.6298 | 12.9604 | 39 |
| 5.4751 | 13.0937 | 40 |
| 5.5724 | 12.8909 | 41 |
| 5.6251 | 13.1132 | 42 |
| 5.5483 | 12.7036 | 43 |
| 5.6252 | 13.1233 | 44 |
| 5.4592 | 13.1353 | 45 |
| 5.5780 | 13.2373 | 46 |
| 5.5350 | 13.4289 | 47 |
| 5.4859 | 13.3994 | 48 |
| 5.6908 | 13.1062 | 49 |
| 5.7516 | 13.1705 | 50 |
| 5.5373 | 13.3196 | 51 |
| 5.6078 | 13.3352 | 52 |
| 5.5998 | 13.3831 | 53 |
| 5.6833 | 13.4430 | 54 |
| 5.6047 | 12.7287 | 55 |
| 5.7165 | 13.1647 | 56 |
| 5.5246 | 13.5831 | 57 |
| 5.5244 | 13.4733 | 58 |
| 5.5659 | 13.8621 | 59 |
| 5.6702 | 13.0873 | 60 |
| 5.5403 | 13.2744 | 61 |
| 5.4980 | 13.5826 | 62 |
| 5.5052 | 13.4584 | 63 |
| 5.5921 | 13.6191 | 64 |
| 5.5647 | 13.2221 | 65 |
| 5.6330 | 13.4804 | 66 |
| 5.6607 | 13.0722 | 67 |
| 5.7957 | 13.6183 | 68 |
| 5.7403 | 13.5204 | 69 |
| 5.5702 | 13.4229 | 70 |
| 5.4891 | 13.6547 | 71 |
| 5.7374 | 13.5464 | 72 |
| 5.6032 | 13.3607 | 73 |
| 5.5891 | 14.0467 | 74 |
| 5.7014 | 13.7621 | 75 |
| 5.6749 | 13.4568 | 76 |
| 5.6180 | 13.7552 | 77 |
| 5.6203 | 13.7563 | 78 |
| 5.6290 | 13.4801 | 79 |
| 5.6179 | 13.6345 | 80 |
| 5.5856 | 13.8037 | 81 |
| 5.6667 | 14.1205 | 82 |
| 5.5012 | 14.2115 | 83 |
| 5.6736 | 13.9032 | 84 |
| 5.6132 | 13.7493 | 85 |
| 5.6931 | 13.5402 | 86 |
| 5.4744 | 13.9974 | 87 |
| 5.6554 | 14.0855 | 88 |
| 5.5775 | 13.7100 | 89 |
| 5.6002 | 13.7944 | 90 |
| 5.6341 | 14.4328 | 91 |
| nan | nan | 92 |
| nan | nan | 93 |
| nan | nan | 94 |
| nan | nan | 95 |
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| nan | nan | 166 |
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| nan | nan | 168 |
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| nan | nan | 171 |
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| nan | nan | 173 |
| nan | nan | 174 |
| nan | nan | 175 |
| nan | nan | 176 |
| nan | nan | 177 |
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| nan | nan | 179 |
| nan | nan | 180 |
| nan | nan | 181 |
| nan | nan | 182 |
| nan | nan | 183 |
| nan | nan | 184 |
| nan | nan | 185 |
| nan | nan | 186 |
| nan | nan | 187 |
| nan | nan | 188 |
| nan | nan | 189 |
| nan | nan | 190 |
| nan | nan | 191 |
| nan | nan | 192 |
| nan | nan | 193 |
| nan | nan | 194 |
| nan | nan | 195 |
| nan | nan | 196 |
| nan | nan | 197 |
| nan | nan | 198 |
| nan | nan | 199 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
antoooooine/Reinforce-Pixelcopter-PLE-v0
|
antoooooine
| 2023-02-02T06:56:56Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T09:34:36Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.70 +/- 52.31
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jha2ee/StableDiffusion_finetuning_SisterIcon
|
jha2ee
| 2023-02-02T06:38:30Z | 8 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-02T06:32:19Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Sister-icon-style Dreambooth model trained by jha2ee
### with [TheLastBen's fast-DreamBooth]
(https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab
[fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
aristeia/q-FrozenLake-v1-4x4-noSlippery
|
aristeia
| 2023-02-02T06:24:49Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-02T06:24:45Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="aristeia/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Brain22/ppo-Huggy
|
Brain22
| 2023-02-02T06:17:08Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-02T06:17:01Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Brain22/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
amrisaurus/pretrained-bert-uncased-50
|
amrisaurus
| 2023-02-02T06:10:39Z | 1 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2023-02-02T06:09:53Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-bert-uncased-50
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pretrained-bert-uncased-50
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.6891
- Validation Loss: 13.0813
- Epoch: 49
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.9180 | 9.5765 | 0 |
| 7.0631 | 9.6436 | 1 |
| 6.5662 | 10.4855 | 2 |
| 6.1181 | 10.4295 | 3 |
| 6.2069 | 10.4589 | 4 |
| 5.9769 | 11.0551 | 5 |
| 6.1633 | 10.8653 | 6 |
| 5.9430 | 11.3191 | 7 |
| 5.7405 | 10.9468 | 8 |
| 5.8629 | 11.1128 | 9 |
| 5.8546 | 11.6032 | 10 |
| 5.7616 | 12.0396 | 11 |
| 5.7221 | 11.4718 | 12 |
| 5.8037 | 11.6265 | 13 |
| 5.8047 | 11.7407 | 14 |
| 5.6255 | 11.9321 | 15 |
| 5.7123 | 11.9664 | 16 |
| 5.7439 | 12.3851 | 17 |
| 5.6358 | 11.7695 | 18 |
| 5.6334 | 12.1840 | 19 |
| 5.7475 | 12.3386 | 20 |
| 5.6651 | 12.1824 | 21 |
| 5.6864 | 12.1818 | 22 |
| 5.7437 | 12.1632 | 23 |
| 5.5457 | 12.3682 | 24 |
| 5.4805 | 12.2731 | 25 |
| 5.7177 | 12.7105 | 26 |
| 5.6454 | 12.3077 | 27 |
| 5.6164 | 12.4352 | 28 |
| 5.8538 | 12.2957 | 29 |
| 5.6449 | 12.4987 | 30 |
| 5.5644 | 12.6280 | 31 |
| 5.8275 | 12.5619 | 32 |
| 5.5706 | 13.0127 | 33 |
| 5.6039 | 12.4849 | 34 |
| 5.6839 | 12.2682 | 35 |
| 5.6085 | 12.5530 | 36 |
| 5.5826 | 12.4190 | 37 |
| 5.5802 | 12.8276 | 38 |
| 5.6272 | 12.9266 | 39 |
| 5.4752 | 13.0355 | 40 |
| 5.5738 | 12.8739 | 41 |
| 5.6231 | 13.1363 | 42 |
| 5.5497 | 12.6590 | 43 |
| 5.6278 | 13.0785 | 44 |
| 5.4599 | 13.0727 | 45 |
| 5.5782 | 13.2001 | 46 |
| 5.5343 | 13.4125 | 47 |
| 5.4846 | 13.3727 | 48 |
| 5.6891 | 13.0813 | 49 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_qnli_192
|
gokuls
| 2023-02-02T05:57:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-02T01:17:40Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_qnli_192
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5701995240710233
---
<!-- 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_sa_GLUE_Experiment_data_aug_qnli_192
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0016
- Accuracy: 0.5702
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5035 | 1.0 | 16604 | 1.0016 | 0.5702 |
| 0.2645 | 2.0 | 33208 | 1.2295 | 0.5724 |
| 0.1684 | 3.0 | 49812 | 1.3804 | 0.5826 |
| 0.1171 | 4.0 | 66416 | 1.5434 | 0.5792 |
| 0.085 | 5.0 | 83020 | 1.5556 | 0.5792 |
| 0.064 | 6.0 | 99624 | 1.7284 | 0.5731 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
FloydianSound/Redline_Diffusion_v2-1
|
FloydianSound
| 2023-02-02T05:50:39Z | 7 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-17T21:56:00Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
## Informations
Fine-tuned SD v2-1 model, 10400 steps, 5 epochs
Aspect Ratio Bucketing centered at 768 resolution, aspect ratio 16:9 (1024x576)
Made with 208 pictures of the movie Redline by MadHouse;
Captions by WD-v1-4
## Tags
Tokens are in the tags.txt along with their occurrences in [#] format
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
FloydianSound/Nixeu_Diffusion_v1-5
|
FloydianSound
| 2023-02-02T05:49:57Z | 14 | 4 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-06T04:09:12Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
## Informations
Fine-tuned SD v1-5 model, 25040 steps, 10 epochs
Aspect Ratio Bucketing centered at 768 resolution
Made with 250 pictures of the artist NIXEU;
if you like the artist support their work on https://www.artstation.com/nixeu - https://www.deviantart.com/nixeu
## Tags
Tokens are in the tags.txt along with their occurrences in [#] format
<img alt="Showcase" src="https://huggingface.co/FloydianSound/Nixeu_Diffusion/resolve/main/00000-nurse%20single%20realistic%20lips%20highres%20fringe%20tall%20image%20absurdres%20long%20hair%20black%20hair%20upper%20body%20dress%20nixeu%20-%201522939414%20-%20Nixeu_Artstyle_nixeu_artstyle_768_e10.png"/>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_mrpc
|
gokuls
| 2023-02-02T05:48:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T23:55:25Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_mrpc
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- 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 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.1838 | 1.0 | 1959 | 0.0138 | 0.9951 | 0.9964 | 0.9958 |
| 0.0406 | 2.0 | 3918 | 0.0055 | 1.0 | 1.0 | 1.0 |
| 0.0267 | 3.0 | 5877 | 0.0129 | 0.9975 | 0.9982 | 0.9979 |
| 0.0151 | 4.0 | 7836 | 0.0004 | 1.0 | 1.0 | 1.0 |
| 0.0108 | 5.0 | 9795 | 0.0104 | 0.9975 | 0.9982 | 0.9979 |
| 0.0075 | 6.0 | 11754 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0059 | 7.0 | 13713 | 0.0005 | 1.0 | 1.0 | 1.0 |
| 0.0047 | 8.0 | 15672 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0033 | 9.0 | 17631 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0031 | 10.0 | 19590 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0025 | 11.0 | 21549 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0019 | 12.0 | 23508 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0019 | 13.0 | 25467 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0014 | 14.0 | 27426 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.001 | 15.0 | 29385 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.001 | 16.0 | 31344 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 17.0 | 33303 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 18.0 | 35262 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 19.0 | 37221 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 20.0 | 39180 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 21.0 | 41139 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 22.0 | 43098 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 23.0 | 45057 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
shivr/dqn-SpaceInvadersNoFrameskip-v4
|
shivr
| 2023-02-02T05:36:20Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-02T05:35:50Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 374.00 +/- 214.89
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shivr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga shivr -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga shivr
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Hyeoni/Question-Generation-Multitask-Korquad
|
Hyeoni
| 2023-02-02T05:17:00Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-08-29T08:51:45Z |
# Question Generation Model with KorQuAD
___
This model is a fine-tuend version of paust/pko-t5-base on the KorQuAD v1.0 Dataset.
### Dataset
KorQuAD v1.0 Dataset (csv)
[Train](https://drive.google.com/file/d/1p0LYPBQE8OW6XRFEW5nxc8P03wgD_plE/view?usp=sharing)
[Valid](https://drive.google.com/file/d/1O0-8BCsYn3PpEmIUjiEBnPz4sBBmQmud/view?usp=sharing)
### Train
30% 확률로 input answer 대신 '[MASK]'를 넣어 질문 문장을 생성하도록 학습한다.
그 결과, input answer가 없을 때도 적절히 answer을 찾아 질문을 생성할 수 있다.
### Question Generation without Input Answer
```python
context = """ CONTEXT """
input_answer = '[MASK]'
generated = generate(best_model, input_answer, context)
show_result(generated)
```
### References
____
Leaf-Question-Generation :https://github.com/KristiyanVachev/Leaf-Question-Generation
pko-t5-base : https://huggingface.co/paust/pko-t5-base
KorQuAD v1.0 : https://korquad.github.io/KorQuad%201.0/
|
DioLiu/autotrain-koles_score-3215890190
|
DioLiu
| 2023-02-02T05:02:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:DioLiu/autotrain-data-koles_score",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-02T05:01:13Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- DioLiu/autotrain-data-koles_score
co2_eq_emissions:
emissions: 0.009007200392120884
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 3215890190
- CO2 Emissions (in grams): 0.0090
## Validation Metrics
- Loss: 1.187
- Accuracy: 0.542
- Macro F1: 0.368
- Micro F1: 0.542
- Weighted F1: 0.482
- Macro Precision: 0.331
- Micro Precision: 0.542
- Weighted Precision: 0.434
- Macro Recall: 0.414
- Micro Recall: 0.542
- Weighted Recall: 0.542
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/DioLiu/autotrain-koles_score-3215890190
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("DioLiu/autotrain-koles_score-3215890190", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("DioLiu/autotrain-koles_score-3215890190", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
culteejen/PPO-default-Roomba
|
culteejen
| 2023-02-02T04:10:01Z | 9 | 2 |
stable-baselines3
|
[
"stable-baselines3",
"Roomba",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-25T22:28:27Z |
---
library_name: stable-baselines3
tags:
- Roomba
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Roomba
type: Roomba
metrics:
- type: mean_reward
value: -132.80 +/- 40.23
name: mean_reward
verified: false
---
# **PPO** Agent playing **Roomba**
This is a trained model of a **PPO** agent playing **Roomba**
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
...
```
|
huggingtweets/wnbagirlfriend
|
huggingtweets
| 2023-02-02T03:34:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-02T03:32:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/wnbagirlfriend/1675308841393/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427129645888114693/HsNIpekZ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">jody</div>
<div style="text-align: center; font-size: 14px;">@wnbagirlfriend</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from jody.
| Data | jody |
| --- | --- |
| Tweets downloaded | 3120 |
| Retweets | 92 |
| Short tweets | 588 |
| Tweets kept | 2440 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/oghnr1wa/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wnbagirlfriend's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/o9d6w49a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/o9d6w49a/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wnbagirlfriend')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
9au5a/nlpandweb
|
9au5a
| 2023-02-02T03:04:38Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-02T03:01:29Z |
---
tags:
- generated_from_trainer
model-index:
- name: result
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. -->
# result
This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) 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: 3e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
FUXI/yuyan-dialogue
|
FUXI
| 2023-02-02T03:01:44Z | 0 | 2 | null |
[
"text-generation",
"dialogue-generation",
"pytorch",
"inference acceleration",
"gpt2",
"gpt3",
"zh",
"arxiv:2005.14165",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2022-12-26T06:05:50Z |
---
license: apache-2.0
language: zh
inference: false
tags:
- text-generation
- dialogue-generation
- pytorch
- inference acceleration
- gpt2
- gpt3
---
# YuYan-Dialogue
YuYan is a series of Chinese language models with different size, developed by Fuxi AI lab, Netease.Inc. They are trained on a large Chinese novel dataset of high quality.
YuYan is in the same family of decoder-only models like [GPT2 and GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
YuYan-Dialogue is a dialogue model by fine-tuning the YuYan-11b on a large multi-turn dialogue dataset of high quality. It has very strong conversation generation capabilities.
## Model Inference Acceleration
As the model size increases, the model inference time increases and more computational resources are required.
Therefore, we developed our own transformer model inference acceleration framework, [EET](https://github.com/NetEase-FuXi/EET.git). More details are in [Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model](https://aclanthology.org/2022.naacl-industry.8/).
We combine our language model with the EET inference framework to provide industrial-grade inference reasoning performance.
## How to use
Our model is trained based on the [fairseq](https://github.com/facebookresearch/fairseq). As a result, the inference and finetuning depend on it.
For inference, we modify some parts of the original fairseq codes. Mainly
> fairseq-0.12.2/fairseq/sequence_generator.py
We integrate the EET with sequence_generator. We replace the eos token to a token unlikely to be sampled to ensure the generated text length. The repetition penalty trick is also modified. You can change the penalty strength by adjusting the value of `self.ban_weight`.
Then, to keep the eos token in the final generated text, we change the line 75 `include_eos=False` to `include_eos=True` in
> fairseq-0.12.2/fairseq/data/dictionary.py
Finally, to pass in parameters in python scripts, we remove the line 67 ~ line 69 in
>fairseq-0.12.2/fairseq/dataclass/utils.py
Below are the install tutorial.
```
# install pytorch
pip install torch==1.8.1 # install pytorch
# install fairseq
unzip fairseq-0.12.2.zip
cd fairseq-0.12.2
pip install.
# install EET
git clone https://github.com/NetEase-FuXi/EET.git
cd EET
pip install .
# install transformers (EET requirements)
pip install transformers==4.23
# make a folder, move the dictionary file and model file into it.
mkdir transformer_lm_gpt2_xxl_dialogue
mv dict.txt transformer_lm_gpt2_xxl_dialogue/
mv checkpoint_best_part_*.pt transformer_lm_gpt2_xxl_dialogue/
```
`inference.py` is a script to provide a interface to initialize the EET object and sequence_generator. It includes some pre-process and post-process functions for text input and output. You can modify the script according to your needs.
In addition, it provide a simple object to organize the dialogue generation and dialogue history.
After the environment is ready, several lines of codes can realize the inference.
``` python
from inference import Inference, Dialogue
model_path = "transformer_lm_gpt2_xxl_dialogue/checkpoint_best.pt"
data_path = "transformer_lm_gpt2_xxl_dialogue"
eet_batch_size = 10 # max inference batch size, adjust according to cuda memory, 40GB memory is necessary
inference = Inference(model_path, data_path, eet_batch_size)
dialogue_model = Dialogue(inference)
dialogue_model.get_repsonse("你好啊")
```
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- https://aclanthology.org/2022.naacl-industry.8/
```
@inproceedings{li-etal-2022-easy,
title = "Easy and Efficient Transformer: Scalable Inference Solution For Large {NLP} Model",
author = "Li, Gongzheng and
Xi, Yadong and
Ding, Jingzhen and
Wang, Duan and
Luo, Ziyang and
Zhang, Rongsheng and
Liu, Bai and
Fan, Changjie and
Mao, Xiaoxi and
Zhao, Zeng",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.8",
doi = "10.18653/v1/2022.naacl-industry.8",
pages = "62--68"
}
```
## Contact Us
You can also contact us by email:
xiyadong@corp.netease.com, dingjingzhen@corp.netease
|
AKFromCanada/Reinforce-CartPole-v1
|
AKFromCanada
| 2023-02-02T02:41:09Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-02T02:41:01Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_mrpc
|
gokuls
| 2023-02-02T02:37:41Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:57:58Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- 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_sa_GLUE_Experiment_data_aug_mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.1488 | 1.0 | 980 | 0.0012 | 1.0 | 1.0 | 1.0 |
| 0.0183 | 2.0 | 1960 | 0.0002 | 1.0 | 1.0 | 1.0 |
| 0.0072 | 3.0 | 2940 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0044 | 4.0 | 3920 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0031 | 5.0 | 4900 | 0.0002 | 1.0 | 1.0 | 1.0 |
| 0.0026 | 6.0 | 5880 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.002 | 7.0 | 6860 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 8.0 | 7840 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 9.0 | 8820 | 0.0077 | 0.9975 | 0.9982 | 0.9979 |
| 0.0015 | 10.0 | 9800 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 11.0 | 10780 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 12.0 | 11760 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 13.0 | 12740 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.001 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 18.0 | 17640 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 27.0 | 26460 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 30.0 | 29400 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 31.0 | 30380 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 32.0 | 31360 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 33.0 | 32340 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 34.0 | 33320 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 35.0 | 34300 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 36.0 | 35280 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 37.0 | 36260 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 38.0 | 37240 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 39.0 | 38220 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Pie31415/dm_anime
|
Pie31415
| 2023-02-02T02:31:05Z | 7 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"dataset:huggan/selfie2anime",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-02-01T23:34:44Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
datasets:
- huggan/selfie2anime
---
# This model is a fine-tuned diffusion model for unconditional image generation of animefaces.
Even after fine-tuning the diffusion model for 10 epochs the generated images are still cursed... 💀. Maybe more epochs would help?
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Pie31415/dm_anime')
image = pipeline().images[0]
image
```
|
rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor-try-2
|
rohitp1
| 2023-02-02T02:20:16Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-01-30T04:30:59Z |
---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Nystrom-W2V2-100hrs-take-4-unfreeze-extractor-try-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Nystrom-W2V2-100hrs-take-4-unfreeze-extractor-try-2
This model is a fine-tuned version of [rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor](https://huggingface.co/rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 27.1915
- Wer: 0.0869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 23.1458 | 9.01 | 1000 | 28.9573 | 0.1039 |
| 32.7156 | 18.02 | 2000 | 25.6155 | 0.1218 |
| 43.506 | 27.03 | 3000 | 27.6332 | 0.1228 |
| 43.3608 | 36.04 | 4000 | 26.0539 | 0.1169 |
| 39.984 | 45.04 | 5000 | 25.9836 | 0.1137 |
| 35.1977 | 54.05 | 6000 | 26.2060 | 0.1077 |
| 30.1951 | 63.06 | 7000 | 27.0999 | 0.1033 |
| 25.7519 | 72.07 | 8000 | 27.8459 | 0.0964 |
| 22.1982 | 81.08 | 9000 | 27.9773 | 0.0908 |
| 20.0551 | 90.09 | 10000 | 27.4222 | 0.0884 |
| 19.4505 | 99.1 | 11000 | 27.1915 | 0.0869 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.11.0
|
aburkard/my_awesome_model
|
aburkard
| 2023-02-02T02:15:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-02T00:12:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 379538407424.0
- Rmse: 616066.875
- Mae: 589504.9375
- Mape: 1.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mae | Mape |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:-----------:|:------:|
| No log | 1.0 | 97 | 379540209664.0 | 616068.375 | 589506.5 | 1.0000 |
| No log | 2.0 | 194 | 379538407424.0 | 616066.875 | 589504.9375 | 1.0000 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
StupidGame/AnythingV4.5
|
StupidGame
| 2023-02-02T02:10:47Z | 21 | 1 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-16T01:13:38Z |
---
license: creativeml-openrail-m
---
|
swl-models/9527
|
swl-models
| 2023-02-02T01:48:23Z | 0 | 14 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-02T00:54:21Z |
---
license: creativeml-openrail-m
---
|
swl-models/DanMix-v1
|
swl-models
| 2023-02-02T01:34:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-02T00:30:09Z |
---
license: creativeml-openrail-m
---
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_mrpc_256
|
gokuls
| 2023-02-02T01:14:12Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:55:04Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_mrpc_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- 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_sa_GLUE_Experiment_data_aug_mrpc_256
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.2052 | 1.0 | 980 | 0.0476 | 0.9853 | 0.9894 | 0.9873 |
| 0.0409 | 2.0 | 1960 | 0.0031 | 1.0 | 1.0 | 1.0 |
| 0.0211 | 3.0 | 2940 | 0.0006 | 1.0 | 1.0 | 1.0 |
| 0.0131 | 4.0 | 3920 | 0.0005 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 5.0 | 4900 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0058 | 6.0 | 5880 | 0.0002 | 1.0 | 1.0 | 1.0 |
| 0.0041 | 7.0 | 6860 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0035 | 8.0 | 7840 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0029 | 9.0 | 8820 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0022 | 10.0 | 9800 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0021 | 11.0 | 10780 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 12.0 | 11760 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 13.0 | 12740 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 18.0 | 17640 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 27.0 | 26460 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 30.0 | 29400 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 31.0 | 30380 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 32.0 | 31360 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 33.0 | 32340 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 34.0 | 33320 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 35.0 | 34300 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 36.0 | 35280 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 37.0 | 36260 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 38.0 | 37240 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 39.0 | 38220 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 40.0 | 39200 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 41.0 | 40180 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 42.0 | 41160 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 43.0 | 42140 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 44.0 | 43120 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 45.0 | 44100 | 0.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 46.0 | 45080 | 0.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola_128
|
gokuls
| 2023-02-02T01:10:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T23:07:52Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola_128
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.10463488919851624
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7034
- Matthews Correlation: 0.1046
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- 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 | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.6386 | 1.0 | 1669 | 0.7034 | 0.1046 |
| 0.5613 | 2.0 | 3338 | 0.7201 | 0.0912 |
| 0.535 | 3.0 | 5007 | 0.7257 | 0.1111 |
| 0.5023 | 4.0 | 6676 | 0.7109 | 0.1655 |
| 0.4569 | 5.0 | 8345 | 0.7769 | 0.1762 |
| 0.4162 | 6.0 | 10014 | 0.7752 | 0.1431 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AdhilB/AI
|
AdhilB
| 2023-02-02T00:57:10Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-02-02T00:53:24Z |
---
title: GFPGAN
emoji: 😁
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 3.1.7
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_mrpc_96
|
gokuls
| 2023-02-02T00:49:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:48:14Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_mrpc_96
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- 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_sa_GLUE_Experiment_data_aug_mrpc_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3242 | 1.0 | 980 | 0.0830 | 0.9804 | 0.9857 | 0.9830 |
| 0.0843 | 2.0 | 1960 | 0.0355 | 0.9828 | 0.9875 | 0.9852 |
| 0.0431 | 3.0 | 2940 | 0.0105 | 1.0 | 1.0 | 1.0 |
| 0.0268 | 4.0 | 3920 | 0.0046 | 1.0 | 1.0 | 1.0 |
| 0.019 | 5.0 | 4900 | 0.0015 | 1.0 | 1.0 | 1.0 |
| 0.0141 | 6.0 | 5880 | 0.0011 | 1.0 | 1.0 | 1.0 |
| 0.0115 | 7.0 | 6860 | 0.0007 | 1.0 | 1.0 | 1.0 |
| 0.0094 | 8.0 | 7840 | 0.0004 | 1.0 | 1.0 | 1.0 |
| 0.0078 | 9.0 | 8820 | 0.0004 | 1.0 | 1.0 | 1.0 |
| 0.0056 | 10.0 | 9800 | 0.0006 | 1.0 | 1.0 | 1.0 |
| 0.0056 | 11.0 | 10780 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0039 | 12.0 | 11760 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0038 | 13.0 | 12740 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0029 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0026 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0025 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0019 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0017 | 18.0 | 17640 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0013 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0012 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.001 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 27.0 | 26460 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 30.0 | 29400 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 31.0 | 30380 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0005 | 32.0 | 31360 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 33.0 | 32340 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 34.0 | 33320 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 35.0 | 34300 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 36.0 | 35280 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 37.0 | 36260 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 38.0 | 37240 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 39.0 | 38220 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 40.0 | 39200 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 41.0 | 40180 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 42.0 | 41160 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 43.0 | 42140 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 44.0 | 43120 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 45.0 | 44100 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 46.0 | 45080 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 47.0 | 46060 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 48.0 | 47040 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 49.0 | 48020 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 50.0 | 49000 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
wybxc/of-diffusion
|
wybxc
| 2023-02-02T00:46:31Z | 0 | 1 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
text-to-image
| 2023-01-30T09:46:30Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: cc-by-nc-sa-4.0
---
# AI 元火娘计划
在线演示：[AI 元火娘 - a Hugging Face Space by wybxc](https://huggingface.co/spaces/wybxc/of-diffusion-demo)
## v1 (Lora)
SDv1 版本适用于 Stable Diffusion v1 系列模型，目前大多数模型都是此类。
SDv2 版本适用于 Stable Diffusion v2 系列模型，如 Waifu Diffusion 1.4 和 PVC 模型。
- 妹妹 - 燕火: [SDv1](https://huggingface.co/wybxc/yanhuo-v1-lora) [SDv2](https://huggingface.co/wybxc/yanhuo-v1-lora-sd2)
- 姐姐 - 燕元: [SDv1](https://huggingface.co/wybxc/yanyuan-v1-lora) [SDv2](https://huggingface.co/wybxc/yanyuan-v1-lora-sd2)
## v1 (Dreambooth)
使用 dreambooth 在 Novel AI final pruned 模型基础上训练，之后融合 10% 的 Anything 3.0 模型。
**在 A1111 的 Web UI 中使用时，需要设置 `Clip skip` 为 2。**
- [妹妹 - 燕火](https://huggingface.co/wybxc/yanhuo-v1-dreambooth)
- [姐姐 - 燕元](https://huggingface.co/wybxc/yanyuan-v1-dreambooth)
- [姐妹同屏](https://huggingface.co/wybxc/yuanhuo-v1-dreambooth)
## 更新日志
- 2023.01.30：第一版模型。
- 2023.02.01：第一版模型追加 Lora 版本。
## 协议
本仓库及下属仓库所包含模型以署名-非商业性使用-相同方式共享 4.0 国际（[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh)）协议公开发布。
模型训练所用数据集来源于元火动漫社社员的创作，版权归原作者与元火动漫社所有，不予公开。
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_mrpc_384
|
gokuls
| 2023-02-02T00:34:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:50:13Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_mrpc_384
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 1.0
- name: F1
type: f1
value: 1.0
---
<!-- 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_sa_GLUE_Experiment_data_aug_mrpc_384
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Combined Score: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---:|:--------------:|
| 0.1771 | 1.0 | 980 | 0.0049 | 1.0 | 1.0 | 1.0 |
| 0.0321 | 2.0 | 1960 | 0.0009 | 1.0 | 1.0 | 1.0 |
| 0.0154 | 3.0 | 2940 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0086 | 4.0 | 3920 | 0.0009 | 1.0 | 1.0 | 1.0 |
| 0.0062 | 5.0 | 4900 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0039 | 6.0 | 5880 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0039 | 7.0 | 6860 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0028 | 8.0 | 7840 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0022 | 9.0 | 8820 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0018 | 10.0 | 9800 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.002 | 11.0 | 10780 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 12.0 | 11760 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0015 | 13.0 | 12740 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 14.0 | 13720 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0011 | 15.0 | 14700 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0008 | 16.0 | 15680 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0009 | 17.0 | 16660 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0007 | 18.0 | 17640 | 0.0001 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 19.0 | 18620 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0006 | 20.0 | 19600 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 21.0 | 20580 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 22.0 | 21560 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 23.0 | 22540 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 24.0 | 23520 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 25.0 | 24500 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0004 | 26.0 | 25480 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0003 | 27.0 | 26460 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 28.0 | 27440 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0002 | 29.0 | 28420 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_cola_256
|
gokuls
| 2023-02-02T00:12:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:28:45Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_cola_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.09390288672705373
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_cola_256
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6609
- Matthews Correlation: 0.0939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- 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 | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.5394 | 1.0 | 1669 | 0.6609 | 0.0939 |
| 0.4545 | 2.0 | 3338 | 0.7807 | 0.0474 |
| 0.4253 | 3.0 | 5007 | 0.8029 | 0.0846 |
| 0.388 | 4.0 | 6676 | 0.8930 | 0.0738 |
| 0.3433 | 5.0 | 8345 | 0.9284 | 0.0834 |
| 0.2986 | 6.0 | 10014 | 1.0809 | 0.1026 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
sammael70/1223
|
sammael70
| 2023-02-02T00:09:41Z | 0 | 0 | null |
[
"es",
"arxiv:1910.09700",
"license:odbl",
"region:us"
] | null | 2023-02-02T00:07:39Z |
---
license: odbl
language:
- es
---
# 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 [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- 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]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### 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:**
[More Information Needed]
**APA:**
[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]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
|
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_cola
|
gokuls
| 2023-02-01T23:54:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:35:34Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.05152844185670031
---
<!-- 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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_cola
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6549
- Matthews Correlation: 0.0515
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- 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 | Matthews Correlation |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.5347 | 1.0 | 1669 | 0.6549 | 0.0515 |
| 0.4507 | 2.0 | 3338 | 0.8182 | 0.0794 |
| 0.407 | 3.0 | 5007 | 0.8573 | 0.0853 |
| 0.3439 | 4.0 | 6676 | 0.9437 | 0.0871 |
| 0.2873 | 5.0 | 8345 | 1.0250 | 0.0530 |
| 0.2424 | 6.0 | 10014 | 1.2340 | 0.0733 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_cola
|
gokuls
| 2023-02-01T23:48:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T23:05:48Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.12240849993250438
---
<!-- 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_sa_GLUE_Experiment_logit_kd_data_aug_cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7299
- Matthews Correlation: 0.1224
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5745 | 1.0 | 835 | 0.7299 | 0.1224 |
| 0.3736 | 2.0 | 1670 | 0.7628 | 0.1626 |
| 0.2919 | 3.0 | 2505 | 0.7388 | 0.1954 |
| 0.2517 | 4.0 | 3340 | 0.7483 | 0.1699 |
| 0.2279 | 5.0 | 4175 | 0.7558 | 0.1651 |
| 0.2108 | 6.0 | 5010 | 0.7734 | 0.1542 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Jedalc/codeparrot-gp2-finetune
|
Jedalc
| 2023-02-01T23:39:15Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-01T18:44:14Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-gp2-finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-gp2-finetune
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7282
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5006 | 0.93 | 5000 | 1.7282 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Lakoc/a2c-PandaReachDense-v2
|
Lakoc
| 2023-02-01T23:19:16Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T23:17:09Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.48 +/- 0.17
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
...
```
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_cola
|
gokuls
| 2023-02-01T22:56:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:27:01Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.12046776548411303
---
<!-- 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_sa_GLUE_Experiment_data_aug_cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8362
- Matthews Correlation: 0.1205
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4726 | 1.0 | 835 | 0.8362 | 0.1205 |
| 0.2428 | 2.0 | 1670 | 1.3000 | 0.1122 |
| 0.1378 | 3.0 | 2505 | 1.3626 | 0.1226 |
| 0.0893 | 4.0 | 3340 | 1.6155 | 0.1608 |
| 0.0648 | 5.0 | 4175 | 1.8098 | 0.0958 |
| 0.049 | 6.0 | 5010 | 2.0187 | 0.1179 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
gokuls/distilbert_sa_GLUE_Experiment_data_aug_cola_192
|
gokuls
| 2023-02-01T22:48:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T22:33:09Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert_sa_GLUE_Experiment_data_aug_cola_192
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.07731897804953623
---
<!-- 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_sa_GLUE_Experiment_data_aug_cola_192
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6791
- Matthews Correlation: 0.0773
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.558 | 1.0 | 835 | 0.6791 | 0.0773 |
| 0.4341 | 2.0 | 1670 | 0.7597 | 0.0700 |
| 0.3665 | 3.0 | 2505 | 0.8224 | 0.0934 |
| 0.3213 | 4.0 | 3340 | 0.8997 | 0.1104 |
| 0.2851 | 5.0 | 4175 | 0.9737 | 0.0851 |
| 0.2544 | 6.0 | 5010 | 1.0495 | 0.1026 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
VeryLost/finetuning-sentiment-model-3000-samples
|
VeryLost
| 2023-02-01T22:48:04Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T19:18:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
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: 0.3161
- Accuracy: 0.87
- F1: 0.8730
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
TolgahanT/TT
|
TolgahanT
| 2023-02-01T22:21:32Z | 0 | 0 |
diffusers
|
[
"diffusers",
"ee",
"dataset:fka/awesome-chatgpt-prompts",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-01T22:18:33Z |
---
license: creativeml-openrail-m
datasets:
- fka/awesome-chatgpt-prompts
language:
- ee
metrics:
- cer
library_name: diffusers
---
|
Lakoc/a2c-AntBulletEnv-v0
|
Lakoc
| 2023-02-01T22:21:15Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T22:20:14Z |
---
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: 1119.45 +/- 345.02
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
...
```
|
tomekkorbak/nostalgic_jones
|
tomekkorbak
| 2023-02-01T22:21:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:tomekkorbak/detoxify-pile-chunk3-0-50000",
"dataset:tomekkorbak/detoxify-pile-chunk3-50000-100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-100000-150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-150000-200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-200000-250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-250000-300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-300000-350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-350000-400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-400000-450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-450000-500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-500000-550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-550000-600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-600000-650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-650000-700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-700000-750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-750000-800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-800000-850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-850000-900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-900000-950000",
"dataset:tomekkorbak/detoxify-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/detoxify-pile-chunk3-1900000-1950000",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-01-31T22:34:53Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/detoxify-pile-chunk3-0-50000
- tomekkorbak/detoxify-pile-chunk3-50000-100000
- tomekkorbak/detoxify-pile-chunk3-100000-150000
- tomekkorbak/detoxify-pile-chunk3-150000-200000
- tomekkorbak/detoxify-pile-chunk3-200000-250000
- tomekkorbak/detoxify-pile-chunk3-250000-300000
- tomekkorbak/detoxify-pile-chunk3-300000-350000
- tomekkorbak/detoxify-pile-chunk3-350000-400000
- tomekkorbak/detoxify-pile-chunk3-400000-450000
- tomekkorbak/detoxify-pile-chunk3-450000-500000
- tomekkorbak/detoxify-pile-chunk3-500000-550000
- tomekkorbak/detoxify-pile-chunk3-550000-600000
- tomekkorbak/detoxify-pile-chunk3-600000-650000
- tomekkorbak/detoxify-pile-chunk3-650000-700000
- tomekkorbak/detoxify-pile-chunk3-700000-750000
- tomekkorbak/detoxify-pile-chunk3-750000-800000
- tomekkorbak/detoxify-pile-chunk3-800000-850000
- tomekkorbak/detoxify-pile-chunk3-850000-900000
- tomekkorbak/detoxify-pile-chunk3-900000-950000
- tomekkorbak/detoxify-pile-chunk3-950000-1000000
- tomekkorbak/detoxify-pile-chunk3-1000000-1050000
- tomekkorbak/detoxify-pile-chunk3-1050000-1100000
- tomekkorbak/detoxify-pile-chunk3-1100000-1150000
- tomekkorbak/detoxify-pile-chunk3-1150000-1200000
- tomekkorbak/detoxify-pile-chunk3-1200000-1250000
- tomekkorbak/detoxify-pile-chunk3-1250000-1300000
- tomekkorbak/detoxify-pile-chunk3-1300000-1350000
- tomekkorbak/detoxify-pile-chunk3-1350000-1400000
- tomekkorbak/detoxify-pile-chunk3-1400000-1450000
- tomekkorbak/detoxify-pile-chunk3-1450000-1500000
- tomekkorbak/detoxify-pile-chunk3-1500000-1550000
- tomekkorbak/detoxify-pile-chunk3-1550000-1600000
- tomekkorbak/detoxify-pile-chunk3-1600000-1650000
- tomekkorbak/detoxify-pile-chunk3-1650000-1700000
- tomekkorbak/detoxify-pile-chunk3-1700000-1750000
- tomekkorbak/detoxify-pile-chunk3-1750000-1800000
- tomekkorbak/detoxify-pile-chunk3-1800000-1850000
- tomekkorbak/detoxify-pile-chunk3-1850000-1900000
- tomekkorbak/detoxify-pile-chunk3-1900000-1950000
model-index:
- name: nostalgic_jones
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. -->
# nostalgic_jones
This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.00056},
'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000',
'tomekkorbak/detoxify-pile-chunk3-50000-100000',
'tomekkorbak/detoxify-pile-chunk3-100000-150000',
'tomekkorbak/detoxify-pile-chunk3-150000-200000',
'tomekkorbak/detoxify-pile-chunk3-200000-250000',
'tomekkorbak/detoxify-pile-chunk3-250000-300000',
'tomekkorbak/detoxify-pile-chunk3-300000-350000',
'tomekkorbak/detoxify-pile-chunk3-350000-400000',
'tomekkorbak/detoxify-pile-chunk3-400000-450000',
'tomekkorbak/detoxify-pile-chunk3-450000-500000',
'tomekkorbak/detoxify-pile-chunk3-500000-550000',
'tomekkorbak/detoxify-pile-chunk3-550000-600000',
'tomekkorbak/detoxify-pile-chunk3-600000-650000',
'tomekkorbak/detoxify-pile-chunk3-650000-700000',
'tomekkorbak/detoxify-pile-chunk3-700000-750000',
'tomekkorbak/detoxify-pile-chunk3-750000-800000',
'tomekkorbak/detoxify-pile-chunk3-800000-850000',
'tomekkorbak/detoxify-pile-chunk3-850000-900000',
'tomekkorbak/detoxify-pile-chunk3-900000-950000',
'tomekkorbak/detoxify-pile-chunk3-950000-1000000',
'tomekkorbak/detoxify-pile-chunk3-1000000-1050000',
'tomekkorbak/detoxify-pile-chunk3-1050000-1100000',
'tomekkorbak/detoxify-pile-chunk3-1100000-1150000',
'tomekkorbak/detoxify-pile-chunk3-1150000-1200000',
'tomekkorbak/detoxify-pile-chunk3-1200000-1250000',
'tomekkorbak/detoxify-pile-chunk3-1250000-1300000',
'tomekkorbak/detoxify-pile-chunk3-1300000-1350000',
'tomekkorbak/detoxify-pile-chunk3-1350000-1400000',
'tomekkorbak/detoxify-pile-chunk3-1400000-1450000',
'tomekkorbak/detoxify-pile-chunk3-1450000-1500000',
'tomekkorbak/detoxify-pile-chunk3-1500000-1550000',
'tomekkorbak/detoxify-pile-chunk3-1550000-1600000',
'tomekkorbak/detoxify-pile-chunk3-1600000-1650000',
'tomekkorbak/detoxify-pile-chunk3-1650000-1700000',
'tomekkorbak/detoxify-pile-chunk3-1700000-1750000',
'tomekkorbak/detoxify-pile-chunk3-1750000-1800000',
'tomekkorbak/detoxify-pile-chunk3-1800000-1850000',
'tomekkorbak/detoxify-pile-chunk3-1850000-1900000',
'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25354],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [25354],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'nostalgic_jones',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output104340',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5070,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/pw7t099z
|
Nonin/ppo-LunarLander-v2
|
Nonin
| 2023-02-01T22:17:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T22:17:32Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.25 +/- 22.65
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
...
```
|
hectorjelly/ppo-LunarLander-v2
|
hectorjelly
| 2023-02-01T22:08:32Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T22:08:12Z |
---
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: 268.23 +/- 21.16
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
...
```
|
stinoco/Taxi-v3
|
stinoco
| 2023-02-01T21:55:04Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T21:55:01Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="stinoco/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
elamdaly/ppo-LunarLander-v2
|
elamdaly
| 2023-02-01T21:33:16Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T21:32:50Z |
---
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: 259.39 +/- 18.20
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
...
```
|
stinoco/q-FrozenLake-v1-4x4-noSlippery
|
stinoco
| 2023-02-01T21:26:10Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T21:15:56Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="stinoco/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ietz/token-paraphrase-MiniLM-L6-v2-baseline
|
ietz
| 2023-02-01T21:08:04Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-02-01T21:05:54Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
tomekkorbak/sad_chandrasekhar
|
tomekkorbak
| 2023-02-01T20:57:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"dataset:tomekkorbak/pii-pile-chunk3-0-50000",
"dataset:tomekkorbak/pii-pile-chunk3-50000-100000",
"dataset:tomekkorbak/pii-pile-chunk3-100000-150000",
"dataset:tomekkorbak/pii-pile-chunk3-150000-200000",
"dataset:tomekkorbak/pii-pile-chunk3-200000-250000",
"dataset:tomekkorbak/pii-pile-chunk3-250000-300000",
"dataset:tomekkorbak/pii-pile-chunk3-300000-350000",
"dataset:tomekkorbak/pii-pile-chunk3-350000-400000",
"dataset:tomekkorbak/pii-pile-chunk3-400000-450000",
"dataset:tomekkorbak/pii-pile-chunk3-450000-500000",
"dataset:tomekkorbak/pii-pile-chunk3-500000-550000",
"dataset:tomekkorbak/pii-pile-chunk3-550000-600000",
"dataset:tomekkorbak/pii-pile-chunk3-600000-650000",
"dataset:tomekkorbak/pii-pile-chunk3-650000-700000",
"dataset:tomekkorbak/pii-pile-chunk3-700000-750000",
"dataset:tomekkorbak/pii-pile-chunk3-750000-800000",
"dataset:tomekkorbak/pii-pile-chunk3-800000-850000",
"dataset:tomekkorbak/pii-pile-chunk3-850000-900000",
"dataset:tomekkorbak/pii-pile-chunk3-900000-950000",
"dataset:tomekkorbak/pii-pile-chunk3-950000-1000000",
"dataset:tomekkorbak/pii-pile-chunk3-1000000-1050000",
"dataset:tomekkorbak/pii-pile-chunk3-1050000-1100000",
"dataset:tomekkorbak/pii-pile-chunk3-1100000-1150000",
"dataset:tomekkorbak/pii-pile-chunk3-1150000-1200000",
"dataset:tomekkorbak/pii-pile-chunk3-1200000-1250000",
"dataset:tomekkorbak/pii-pile-chunk3-1250000-1300000",
"dataset:tomekkorbak/pii-pile-chunk3-1300000-1350000",
"dataset:tomekkorbak/pii-pile-chunk3-1350000-1400000",
"dataset:tomekkorbak/pii-pile-chunk3-1400000-1450000",
"dataset:tomekkorbak/pii-pile-chunk3-1450000-1500000",
"dataset:tomekkorbak/pii-pile-chunk3-1500000-1550000",
"dataset:tomekkorbak/pii-pile-chunk3-1550000-1600000",
"dataset:tomekkorbak/pii-pile-chunk3-1600000-1650000",
"dataset:tomekkorbak/pii-pile-chunk3-1650000-1700000",
"dataset:tomekkorbak/pii-pile-chunk3-1700000-1750000",
"dataset:tomekkorbak/pii-pile-chunk3-1750000-1800000",
"dataset:tomekkorbak/pii-pile-chunk3-1800000-1850000",
"dataset:tomekkorbak/pii-pile-chunk3-1850000-1900000",
"dataset:tomekkorbak/pii-pile-chunk3-1900000-1950000",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-02-01T06:40:06Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- tomekkorbak/pii-pile-chunk3-0-50000
- tomekkorbak/pii-pile-chunk3-50000-100000
- tomekkorbak/pii-pile-chunk3-100000-150000
- tomekkorbak/pii-pile-chunk3-150000-200000
- tomekkorbak/pii-pile-chunk3-200000-250000
- tomekkorbak/pii-pile-chunk3-250000-300000
- tomekkorbak/pii-pile-chunk3-300000-350000
- tomekkorbak/pii-pile-chunk3-350000-400000
- tomekkorbak/pii-pile-chunk3-400000-450000
- tomekkorbak/pii-pile-chunk3-450000-500000
- tomekkorbak/pii-pile-chunk3-500000-550000
- tomekkorbak/pii-pile-chunk3-550000-600000
- tomekkorbak/pii-pile-chunk3-600000-650000
- tomekkorbak/pii-pile-chunk3-650000-700000
- tomekkorbak/pii-pile-chunk3-700000-750000
- tomekkorbak/pii-pile-chunk3-750000-800000
- tomekkorbak/pii-pile-chunk3-800000-850000
- tomekkorbak/pii-pile-chunk3-850000-900000
- tomekkorbak/pii-pile-chunk3-900000-950000
- tomekkorbak/pii-pile-chunk3-950000-1000000
- tomekkorbak/pii-pile-chunk3-1000000-1050000
- tomekkorbak/pii-pile-chunk3-1050000-1100000
- tomekkorbak/pii-pile-chunk3-1100000-1150000
- tomekkorbak/pii-pile-chunk3-1150000-1200000
- tomekkorbak/pii-pile-chunk3-1200000-1250000
- tomekkorbak/pii-pile-chunk3-1250000-1300000
- tomekkorbak/pii-pile-chunk3-1300000-1350000
- tomekkorbak/pii-pile-chunk3-1350000-1400000
- tomekkorbak/pii-pile-chunk3-1400000-1450000
- tomekkorbak/pii-pile-chunk3-1450000-1500000
- tomekkorbak/pii-pile-chunk3-1500000-1550000
- tomekkorbak/pii-pile-chunk3-1550000-1600000
- tomekkorbak/pii-pile-chunk3-1600000-1650000
- tomekkorbak/pii-pile-chunk3-1650000-1700000
- tomekkorbak/pii-pile-chunk3-1700000-1750000
- tomekkorbak/pii-pile-chunk3-1750000-1800000
- tomekkorbak/pii-pile-chunk3-1800000-1850000
- tomekkorbak/pii-pile-chunk3-1850000-1900000
- tomekkorbak/pii-pile-chunk3-1900000-1950000
model-index:
- name: sad_chandrasekhar
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. -->
# sad_chandrasekhar
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.0},
'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 4096,
'prefix': '<|aligned|>'}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'sad_chandrasekhar',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 5035,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1nikqva5
|
Aphophis420/stargate-diffusion-sg1-1
|
Aphophis420
| 2023-02-01T20:53:57Z | 7 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-12-30T15:09:33Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### stargate-diffusion-sg1-1 Dreambooth model trained by Aphophis420 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
USE: *prompt*, still from stargate sg1
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
|
Sartc/PPO-2FEB-LunarLander-v2
|
Sartc
| 2023-02-01T20:47:15Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T20:44:12Z |
---
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: -402.40 +/- 104.21
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
...
```
|
LowGI/my_new_asr_model
|
LowGI
| 2023-02-01T20:26:57Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-01T20:15:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: my_new_asr_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_new_asr_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9912
- Wer: 0.9915
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log | 200.0 | 200 | 3.2498 | 0.9972 |
| No log | 400.0 | 400 | 4.1645 | 1.1339 |
| 1.1325 | 600.0 | 600 | 4.7252 | 1.1197 |
| 1.1325 | 800.0 | 800 | 4.9678 | 1.0370 |
| 0.0747 | 1000.0 | 1000 | 4.9912 | 0.9915 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Deisler/Reinforce-Cartoole-01
|
Deisler
| 2023-02-01T20:10:20Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-31T16:10:35Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartoole-01
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 487.60 +/- 37.20
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
OGSneakybot/ppo-LunarLander-v2
|
OGSneakybot
| 2023-02-01T19:36:14Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T17:49:00Z |
---
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: 269.12 +/- 17.42
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
...
```
|
dlu66061/distilbert-base-uncased-finetuned-cola
|
dlu66061
| 2023-02-01T19:36:11Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-01-31T21:05:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5429064789214383
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8171
- Matthews Correlation: 0.5429
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5262 | 1.0 | 535 | 0.5236 | 0.4004 |
| 0.3571 | 2.0 | 1070 | 0.5287 | 0.5073 |
| 0.2325 | 3.0 | 1605 | 0.5771 | 0.5206 |
| 0.1735 | 4.0 | 2140 | 0.7643 | 0.5321 |
| 0.13 | 5.0 | 2675 | 0.8171 | 0.5429 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.0.dev0
|
LowGI/my_asr_model
|
LowGI
| 2023-02-01T19:35:53Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-01T18:36:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: my_asr_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_asr_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2724
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 2.9472 | 20.0 | 100 | 3.4756 | 1.0 |
| 2.9405 | 40.0 | 200 | 3.4197 | 1.0 |
| 2.9322 | 60.0 | 300 | 3.2967 | 1.0 |
| 2.9338 | 80.0 | 400 | 3.4891 | 1.0 |
| 2.9243 | 100.0 | 500 | 3.2724 | 1.0 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Crataco/Pythia-70M-Deduped-Adventure
|
Crataco
| 2023-02-01T19:29:20Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-01-31T23:22:25Z |
---
tags:
- generated_from_trainer
model-index:
- name: pythia-70m-deduped-aid
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. -->
# pythia-70m-deduped-aid
## Model description
This model is a finetune of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) (from when it was instead `pythia-19m-deduped`), on the [`text_adventures.txt`](https://github.com/Latitude-Archives/AIDungeon/blob/ca098ca7dab480d24e47954c8873b03ba1091ffc/data/text_adventures.txt) dataset originally intended for AI Dungeon 2. Performance will be very poor, as expected by the small model, and generations may be offensive thanks to its training data.
This model was trained for testing purposes and was intended for use with KoboldAI. A temperature of `0.5` and a repetition penalty of `1.01` were tested.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ArtYac/Reinforce-CartPole8
|
ArtYac
| 2023-02-01T19:21:13Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T19:21:03Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole8
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
deepparag/Aeona-Beta-New
|
deepparag
| 2023-02-01T18:44:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-24T16:09:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Aeona-Beta-New
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. -->
# Aeona-Beta-New
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5170
## 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: 9
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6794 | 1.0 | 7463 | 3.5170 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
rodrigobrand/lcmnpm
|
rodrigobrand
| 2023-02-01T18:16:51Z | 11 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-01T18:05:51Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### LCMNPM Dreambooth model trained by rodrigobrand with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
qgallouedec/a2c-PandaPushJointsDense-v2
|
qgallouedec
| 2023-02-01T18:07:01Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaPushJointsDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T18:03:23Z |
---
library_name: stable-baselines3
tags:
- PandaPushJointsDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPushJointsDense-v2
type: PandaPushJointsDense-v2
metrics:
- type: mean_reward
value: -8.76 +/- 4.85
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPushJointsDense-v2**
This is a trained model of a **A2C** agent playing **PandaPushJointsDense-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
...
```
|
epinnock/flan-t5-xl-codeparrot-xlcost-text-to-code
|
epinnock
| 2023-02-01T17:59:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xlcost-text-to-code",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-01-31T12:40:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xlcost-text-to-code
model-index:
- name: flan-t5-xl-codeparrot-xlcost-text-to-code
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-xl-codeparrot-xlcost-text-to-code
This model is a fine-tuned version of [epinnock/flan-t5-xl-codeparrot-xlcost-text-to-code](https://huggingface.co/epinnock/flan-t5-xl-codeparrot-xlcost-text-to-code) on the xlcost-text-to-code dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.9876
- eval_rouge1: 43.1227
- eval_rouge2: 25.6539
- eval_rougeL: 41.8635
- eval_rougeLsum: 41.8883
- eval_gen_len: 9.0445
- eval_runtime: 1137.2469
- eval_samples_per_second: 7.17
- eval_steps_per_second: 0.897
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0+cu116
- Datasets 2.9.0
- Tokenizers 0.12.1
|
Kanr1u/rose_charlotte
|
Kanr1u
| 2023-02-01T17:42:27Z | 38 | 0 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain",
"vision",
"dataset:Kanr1u/autotrain-data-emma2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-02-01T17:39:56Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- Kanr1u/autotrain-data-emma2
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 2.1409787540187346
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 3206689984
- CO2 Emissions (in grams): 2.1410
## Validation Metrics
- Loss: 0.303
- Accuracy: 0.846
- Precision: 0.846
- Recall: 0.846
- AUC: 0.929
- F1: 0.846
|
jmeneu/ppo-LunarLander-v2
|
jmeneu
| 2023-02-01T17:41:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-26T16:26:33Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.05 +/- 19.58
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
...
```
|
Mayhem50/sgpt-bloom-560M-nli
|
Mayhem50
| 2023-02-01T17:26:46Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bloom",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-02-01T17:22:23Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MNRLGradCache`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 880,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.00032
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 881,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BloomModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
LizSando/ppo-LunarLander-v2
|
LizSando
| 2023-02-01T17:19:47Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-31T20:39:50Z |
---
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: 262.47 +/- 16.67
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
...
```
|
Cynthia0510/pegasus-samsum
|
Cynthia0510
| 2023-02-01T17:10:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-01T16:10:31Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4812
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6928 | 0.54 | 500 | 1.4812 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
ongkn/q-FrozenLake-v1-4x4-Slippery
|
ongkn
| 2023-02-01T16:59:59Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T16:59:56Z |
---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.77 +/- 0.42
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ongkn/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
krecceg/ppo-Huggy
|
krecceg
| 2023-02-01T16:58:26Z | 23 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-02-01T16:58:19Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: krecceg/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DucHaiten/DucHaitenAnimated
|
DucHaiten
| 2023-02-01T16:46:14Z | 30 | 13 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"image-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-01-20T16:30:54Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
- image-to-image
- diffusers
license: creativeml-openrail-m
inference: true
---
|
YoriV/ppo-LunarLander-v2
|
YoriV
| 2023-02-01T16:39:35Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T15:43:55Z |
---
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: 280.88 +/- 16.27
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
...
```
|
dn-gh/Q-Taxi-v3-1
|
dn-gh
| 2023-02-01T16:39:25Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T16:39:22Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-Taxi-v3-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dn-gh/Q-Taxi-v3-1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
dn-gh/q-FrozenLake-v1-4x4-noSlippery
|
dn-gh
| 2023-02-01T16:26:46Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T16:26:44Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dn-gh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
kiri1701/bert-base-uncased-issues-128-issues-128
|
kiri1701
| 2023-02-01T16:11:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-02-01T15:04:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2456
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0986 | 1.0 | 291 | 1.6929 |
| 1.6401 | 2.0 | 582 | 1.4304 |
| 1.4881 | 3.0 | 873 | 1.3916 |
| 1.4 | 4.0 | 1164 | 1.3796 |
| 1.3416 | 5.0 | 1455 | 1.2012 |
| 1.2807 | 6.0 | 1746 | 1.2733 |
| 1.2396 | 7.0 | 2037 | 1.2646 |
| 1.1993 | 8.0 | 2328 | 1.2098 |
| 1.1661 | 9.0 | 2619 | 1.1862 |
| 1.1406 | 10.0 | 2910 | 1.2223 |
| 1.1294 | 11.0 | 3201 | 1.2056 |
| 1.1042 | 12.0 | 3492 | 1.1655 |
| 1.0827 | 13.0 | 3783 | 1.2525 |
| 1.0738 | 14.0 | 4074 | 1.1685 |
| 1.0626 | 15.0 | 4365 | 1.1182 |
| 1.0629 | 16.0 | 4656 | 1.2456 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
summervent/speller-t5-big-3
|
summervent
| 2023-02-01T16:07:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-01T09:00:43Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: speller-t5-big-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speller-t5-big-3
This model is a fine-tuned version of [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1829
- Rouge1: 27.4616
- Rouge2: 11.1083
- Rougel: 27.5146
- Rougelsum: 27.3079
- Gen Len: 39.1171
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0936 | 0.04 | 500 | 0.5587 | 23.1392 | 7.0032 | 23.1709 | 23.1908 | 41.1081 |
| 0.8042 | 0.07 | 1000 | 0.4168 | 25.1867 | 8.9696 | 25.2993 | 25.1779 | 43.6486 |
| 0.634 | 0.11 | 1500 | 0.3611 | 26.0366 | 8.521 | 26.1568 | 25.9359 | 40.2613 |
| 0.5041 | 0.14 | 2000 | 0.3255 | 26.1019 | 8.7002 | 26.2473 | 25.983 | 40.7928 |
| 0.5279 | 0.18 | 2500 | 0.3041 | 26.1352 | 8.6265 | 26.2606 | 25.9482 | 39.6216 |
| 0.4838 | 0.22 | 3000 | 0.2784 | 26.6137 | 9.8094 | 26.8372 | 26.5692 | 39.3694 |
| 0.4512 | 0.25 | 3500 | 0.2700 | 25.6152 | 9.5832 | 25.7503 | 25.6898 | 38.7387 |
| 0.4412 | 0.29 | 4000 | 0.2612 | 25.6113 | 9.6697 | 25.7482 | 25.6838 | 39.1171 |
| 0.405 | 0.33 | 4500 | 0.2426 | 26.5151 | 9.6882 | 26.7719 | 26.4825 | 39.1892 |
| 0.3987 | 0.36 | 5000 | 0.2390 | 26.479 | 9.6144 | 26.6499 | 26.3759 | 39.0991 |
| 0.407 | 0.4 | 5500 | 0.2325 | 26.4499 | 9.6544 | 26.6649 | 26.3821 | 39.3784 |
| 0.406 | 0.43 | 6000 | 0.2266 | 26.6224 | 9.875 | 26.8468 | 26.6058 | 38.6486 |
| 0.3827 | 0.47 | 6500 | 0.2213 | 26.8997 | 10.0139 | 27.1249 | 26.8252 | 39.1712 |
| 0.334 | 0.51 | 7000 | 0.2247 | 26.7779 | 9.9399 | 26.9951 | 26.6453 | 39.7207 |
| 0.3463 | 0.54 | 7500 | 0.2145 | 26.879 | 9.9911 | 27.0863 | 26.7372 | 39.2432 |
| 0.3439 | 0.58 | 8000 | 0.2102 | 26.8839 | 10.0139 | 27.0715 | 26.7186 | 39.3694 |
| 0.3644 | 0.61 | 8500 | 0.2050 | 26.9076 | 10.0704 | 27.1328 | 26.8411 | 39.2252 |
| 0.3161 | 0.65 | 9000 | 0.2008 | 26.9219 | 10.1927 | 27.1542 | 26.8697 | 38.7928 |
| 0.3273 | 0.69 | 9500 | 0.2018 | 26.8221 | 9.9879 | 27.0473 | 26.7137 | 39.1892 |
| 0.3423 | 0.72 | 10000 | 0.1992 | 26.8572 | 10.0937 | 27.0701 | 26.7469 | 39.2342 |
| 0.3129 | 0.76 | 10500 | 0.1964 | 26.9076 | 10.0704 | 27.1328 | 26.8411 | 39.1712 |
| 0.2841 | 0.79 | 11000 | 0.1937 | 27.4202 | 10.9493 | 27.5146 | 27.2724 | 39.1261 |
| 0.2865 | 0.83 | 11500 | 0.1901 | 27.4559 | 11.0314 | 27.5146 | 27.3022 | 39.2072 |
| 0.2747 | 0.87 | 12000 | 0.1862 | 27.4127 | 10.9878 | 27.5146 | 27.2611 | 38.9459 |
| 0.2766 | 0.9 | 12500 | 0.1905 | 27.4616 | 11.1083 | 27.5146 | 27.3079 | 39.0991 |
| 0.3 | 0.94 | 13000 | 0.1866 | 27.4616 | 11.1083 | 27.5146 | 27.3079 | 39.0541 |
| 0.2729 | 0.98 | 13500 | 0.1829 | 27.4616 | 11.1083 | 27.5146 | 27.3079 | 39.1171 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Lakoc/ppo-Pyramids
|
Lakoc
| 2023-02-01T15:59:08Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-02-01T15:59:01Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: Lakoc/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Tortoise17/whisper-small-hi
|
Tortoise17
| 2023-02-01T15:56:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-02-01T15:54:37Z |
---
language:
- hi
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Squirz/phase2
|
Squirz
| 2023-02-01T15:53:16Z | 7 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-02-01T15:50:51Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Phase2 Dreambooth model trained by Squirz with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
loresiensis/distilbert_classificator
|
loresiensis
| 2023-02-01T15:17:39Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T15:16:50Z |
---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
model-index:
- name: distilbert_classificator
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: test
args: emotion
metrics:
- name: Accuracy
type: accuracy
value: 0.7909922589725545
---
<!-- 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_classificator
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8627
- Accuracy: 0.7910
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 408 | 0.6174 | 0.7882 |
| 0.6884 | 2.0 | 816 | 0.7010 | 0.7945 |
| 0.3202 | 3.0 | 1224 | 0.8627 | 0.7910 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
YashGajjar/RL_agent_lunarlander_starship
|
YashGajjar
| 2023-02-01T15:15:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T15:15:21Z |
---
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: 288.94 +/- 20.75
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
...
```
|
hucruz/custom-textcat-model-viajes
|
hucruz
| 2023-02-01T15:11:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-02-01T14:42:34Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: custom-textcat-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# custom-textcat-model
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the custom dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3305
- Accuracy: 0.9541
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 209 | 0.3650 | 0.9514 |
| No log | 2.0 | 418 | 0.3371 | 0.9568 |
| 0.0108 | 3.0 | 627 | 0.3305 | 0.9541 |
| 0.0108 | 4.0 | 836 | 0.3465 | 0.9568 |
| 0.0056 | 5.0 | 1045 | 0.3498 | 0.9541 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Ashraf-kasem/gpt2_frame_text_predictor
|
Ashraf-kasem
| 2023-02-01T15:06:28Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-02-01T14:27:58Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Ashraf-kasem/gpt2_frame_text_predictor
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Ashraf-kasem/gpt2_frame_text_predictor
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.9203
- Validation Loss: 8.7222
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'LinearWarmup', 'config': {'after_warmup_lr_sched': {'initial_learning_rate': 5e-05, 'decay_steps': 16, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'warmup_steps': 1, 'warmup_learning_rate': 0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.9203 | 8.7222 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
leenw2/Taxi-v3
|
leenw2
| 2023-02-01T15:04:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T15:04:55Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="leenw2/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
leenw2/q-FrozenLake-v1-4x4-noSlippery
|
leenw2
| 2023-02-01T15:02:34Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-02-01T15:02:31Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="leenw2/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
swl-models/toooajk-yagurumagiku-v6
|
swl-models
| 2023-02-01T14:44:08Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-02-01T01:28:29Z |
---
license: creativeml-openrail-m
---
|
swl-models/toooajk-yagurumagiku-v7
|
swl-models
| 2023-02-01T14:43:37Z | 87 | 1 |
diffusers
|
[
"diffusers",
"art",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-02-01T14:43:08Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
duplicated_from: Toooajk/Cornflower_v7
---
Cornflower is a comprehensive painting model based on StableDiffusion, trained with specific styles of illustration and merged with multiple models, which is theoretically somewhat different from real-life human painters.
**Since the Cornflower model contains multiple files, you need to place all the files in the appropriate locations.**
### How to install?
**'cornflower_v7.safetensors'** and **vae file** are placed in the Stable Diffusion model directory.
The .pt files in **'embeddings'** folder are placed in the embeddings directory.
**'cornflower_v7_phantom.pt'** in hypernetwork folder is placed in the Hypernetworks model directory.
### How to use?
After the installation is complete, open webui and switch checkpoint to 'cornflower_v7.safetensors', Hypernetwork to 'cornflower_v7_phantom'.
The following parameters are recommended, and the sampler recommends DPM2 a Karras.
Steps: 20, Sampler: DPM2 a Karras, CFG scale: 7, Size: 640x960, Clip skip: 2, ENSD: 31337
|
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