modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-31 00:44:29
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-31 00:43:54
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
Brizape/SETH_10e-5_29_03
|
Brizape
| 2023-03-29T12:04:23Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-29T12:01:04Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: SETH_10e-5_29_03
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. -->
# SETH_10e-5_29_03
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0704
- Precision: 0.7565
- Recall: 0.8021
- F1: 0.7786
- Accuracy: 0.9831
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2855 | 0.96 | 25 | 0.1080 | 0.7375 | 0.1015 | 0.1785 | 0.9600 |
| 0.0776 | 1.92 | 50 | 0.0720 | 0.5057 | 0.7659 | 0.6092 | 0.9740 |
| 0.0497 | 2.88 | 75 | 0.0557 | 0.6626 | 0.8382 | 0.7401 | 0.9792 |
| 0.0377 | 3.85 | 100 | 0.0620 | 0.6751 | 0.8296 | 0.7444 | 0.9800 |
| 0.0346 | 4.81 | 125 | 0.0652 | 0.6652 | 0.8072 | 0.7294 | 0.9773 |
| 0.0273 | 5.77 | 150 | 0.0643 | 0.6729 | 0.8640 | 0.7566 | 0.9801 |
| 0.0208 | 6.73 | 175 | 0.0720 | 0.6709 | 0.8244 | 0.7398 | 0.9795 |
| 0.0156 | 7.69 | 200 | 0.0623 | 0.6996 | 0.8176 | 0.7540 | 0.9813 |
| 0.0115 | 8.65 | 225 | 0.0733 | 0.6721 | 0.8571 | 0.7534 | 0.9788 |
| 0.0088 | 9.62 | 250 | 0.0704 | 0.7565 | 0.8021 | 0.7786 | 0.9831 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
nullzero-live/bert-base-banking77-pt2
|
nullzero-live
| 2023-03-29T12:00:18Z | 69 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T10:04:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9290417627851566
---
<!-- 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-banking77-pt2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2990
- F1: 0.9290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0285 | 1.0 | 626 | 0.7603 | 0.8517 |
| 0.3662 | 2.0 | 1252 | 0.3676 | 0.9198 |
| 0.1822 | 3.0 | 1878 | 0.2990 | 0.9290 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
stelladk/PPO-SampleFactory-vizdoom_health_gathering_supreme
|
stelladk
| 2023-03-29T11:51:33Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T11:50:57Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.55 +/- 3.81
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r stelladk/PPO-SampleFactory-vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=PPO-SampleFactory-vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=PPO-SampleFactory-vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
NorwegianGoat/finetuning-sentiment-model-3000-samples
|
NorwegianGoat
| 2023-03-29T11:41:11Z | 64 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T11:19:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
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 the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3234
- eval_accuracy: 0.8733
- eval_f1: 0.875
- eval_runtime: 5.399
- eval_samples_per_second: 55.566
- eval_steps_per_second: 3.519
- epoch: 2.0
- step: 376
## 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
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
sonny-dev/taxi
|
sonny-dev
| 2023-03-29T11:32:31Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T11:32:18Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sonny-dev/taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Ashkan92/finetuning-sentiment-model-3000-samples
|
Ashkan92
| 2023-03-29T11:30:33Z | 66 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T11:15:04Z |
---
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: 0.3164
- Accuracy: 0.8667
- F1: 0.8684
## 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 4.27.3
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
akarshh/finetuning-sentiment-model-3000-samples
|
akarshh
| 2023-03-29T11:29:51Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T11:17:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8566666666666667
- name: F1
type: f1
value: 0.8590163934426229
---
<!-- 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 the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3456
- Accuracy: 0.8567
- F1: 0.8590
## 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 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
BeatriceVergati/finetuning-sentiment-model-3000-samples
|
BeatriceVergati
| 2023-03-29T11:29:45Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T11:17:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8833333333333333
- name: F1
type: f1
value: 0.8852459016393444
---
<!-- 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 the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2915
- Accuracy: 0.8833
- F1: 0.8852
## 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 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
senjakala/finetuning-sentiment-model-5000-samples
|
senjakala
| 2023-03-29T11:23:34Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T11:13:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-5000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.89
- name: F1
type: f1
value: 0.8902195608782435
---
<!-- 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-5000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3248
- Accuracy: 0.89
- F1: 0.8902
## 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 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
enriquemarenco/finetuning-sentiment-model-3000-samples
|
enriquemarenco
| 2023-03-29T11:20:51Z | 61 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T11:14:52Z |
---
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: 0.2916
- Accuracy: 0.8833
- F1: 0.8852
## 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 4.27.3
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
omarcevi/ppo-SnowballTarget
|
omarcevi
| 2023-03-29T11:19:06Z | 10 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-03-29T11:19:00Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: omarcevi/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
glundquist/finetuning-sentiment-model-3000-samples
|
glundquist
| 2023-03-29T11:11:43Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T11:05:16Z |
---
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: 0.3296
- Accuracy: 0.8667
- F1: 0.8667
## 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 4.27.3
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
FBM/Reinforce-Pixelcopter-PLE-v0
|
FBM
| 2023-03-29T11:08:26Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-01-05T11:46:25Z |
---
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: 68.30 +/- 43.00
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
|
asenella/reproducing_mopoe
|
asenella
| 2023-03-29T11:05:38Z | 0 | 0 |
pythae
|
[
"pythae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-03-29T11:05:30Z |
---
language: en
tags:
- pythae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
alesthehuman/ppo-Huggy
|
alesthehuman
| 2023-03-29T11:05:20Z | 11 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-03-29T11:05:13Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: alesthehuman/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
egonrp/gpt2-medium-squadv11-portuguese
|
egonrp
| 2023-03-29T10:57:02Z | 167 | 5 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"pt",
"dataset:squad",
"dataset:squad_v1_pt",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-28T22:53:44Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: results_v4c_gpt_medium_original_no_eval
results: []
datasets:
- squad
- squad_v1_pt
language:
- pt
library_name: transformers
inference:
parameters:
do_sample: false
max_new_tokens: 120
widget:
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Onde foi descoberta a Covid-19?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Onde a COVID-19 foi identificada pela primeira vez?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando a COVID-19 foi identificada pela primeira vez?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando a doença foi reportada pela primeira vez?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>"
- text: "<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Em que ano a doença foi identificada pela primeira vez?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Quem foi o autor dos livros Game of Thrones?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Quem foi o escritor dos livros Game of Thrones?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Quem são os produtores executivos da série de TV Game of Thrones?<|assistant|>"
- text: "<|prompter|>Game of Thrones é uma série de TV produzida pelo canal de televisão a cabo HBO. É baseada na série de romances As Crônicas de Gelo e Fogo, escrita por George R.R. Martin, que é produtor, consultor criativo e roteirista da série de TV. David Benioff e D.B. Weiss criaram a série de TV e são produtores executivos, e escritores principais.A série consiste em oito temporadas totalmente transmitidas, compreendendo setenta e três episódios no total.A produção da série é baseada em Belfast, Irlanda do Norte, principalmente no Paint Hall Studios. É a maior e mais cara produção de televisão já montada na Irlanda do Norte. As filmagens da série também foram realizadas em Malta, Islândia, Croácia, Marrocos, Espanha e EUA. Onde foram realizadas as filmagens da série Game of Thrones?<|assistant|>"
- text: '<|prompter|>O sistema de bibliotecas da universidade é dividido entre a biblioteca principal e cada uma das faculdades e escolas. O edifício principal é a Biblioteca Theodore M. Hesburgh, de 14 andares, concluída em 1963, que é o terceiro edifício a abrigar a principal coleção de livros. A frente da biblioteca é decorada com o mural da Palavra da Vida, projetado pelo artista Millard Sheets. Este mural é conhecido popularmente como "Touchdown Jesus" devido à sua proximidade com o Estádio Notre Dame e os braços de Jesus aparecendo para sinalizar um touchdown. Quantos andares possui a Biblioteca Theodore M. Hesburgh?<|assistant|>'
- text: '<|prompter|>O sistema de bibliotecas da universidade é dividido entre a biblioteca principal e cada uma das faculdades e escolas. O edifício principal é a Biblioteca Theodore M. Hesburgh, de 14 andares, concluída em 1963, que é o terceiro edifício a abrigar a principal coleção de livros. A frente da biblioteca é decorada com o mural da Palavra da Vida, projetado pelo artista Millard Sheets. Este mural é conhecido popularmente como "Touchdown Jesus" devido à sua proximidade com o Estádio Notre Dame e os braços de Jesus aparecendo para sinalizar um touchdown. Em que ano a Biblioteca Theodore M. Hesburgh em Notre Dame terminou?<|assistant|>'
- text: '<|prompter|>Rick Grimes é o xerife de uma pequena cidade do estado da Georgia, quando certo dia, é baleado por criminosos durante uma perseguição e entra em coma. Semanas depois, ele acorda em um hospital abandonado e totalmente danificado. Ao sair do hospital, Rick se encontra em um mundo pós-apocalíptico dominado por mortos-vivos. Depois de conhecer Morgan Jones e seu filho, Duane, que lhe explica o novo mundo, Rick decide ir para Atlanta atrás de sua família, onde um possível centro de refugiados foi montado pela Guarda Nacional. Ao chegar em Atlanta, ele logo descobre que a cidade está vazia e foi dominada pelos mortos. Quem o xerife Rick Grimes conheceu?<|assistant|>'
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-medium-squadv11-portuguese
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on squad_v1.1_pt dataset.
** It's a chatbot experiment. ;)
The model was trained in 12 hours on a NVIDIA RTX 3060 12GB.
### Usage:
```
$ python3
>>> from transformers import pipeline, set_seed
>>> set_seed(42)
>>> generator = pipeline('text-generation', model="egonrp/gpt2-medium-squadv11-portuguese")
>>> result = generator('<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>', max_new_tokens=110, num_return_sequences=1, do_sample=False)
>>> print(result)
[{'generated_text': '<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>31 de dezembro do mesmo ano'}]
```
### Usage.2:
```
$ python3
>>> from transformers import GPT2LMHeadModel, GPT2Tokenizer, set_seed
>>> set_seed(42)
>>> model = GPT2LMHeadModel.from_pretrained("egonrp/gpt2-medium-squadv11-portuguese")
>>> tokenizer = GPT2Tokenizer.from_pretrained("egonrp/gpt2-medium-squadv11-portuguese")
>>> tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
>>> model.config.pad_token_id = tokenizer.eos_token_id
>>> prompt_text = '<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>'
>>> encoded_prompt = tokenizer.encode(prompt_text, return_tensors="pt")
>>> output_sequences = model.generate(
input_ids=encoded_prompt,
do_sample=False,
num_return_sequences=1,
max_new_tokens=110,
eos_token_id=model.config.eos_token_id,
pad_token_id=model.config.eos_token_id
)
>>> decoded_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
>>> print(decoded_text)
<|prompter|>A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano. Quando foi reportado o primeiro caso de COVID-19?<|assistant|>31 de dezembro do mesmo ano
```
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
```
git clone -b v4.27-release https://github.com/huggingface/transformers.git
cd transformers/examples/pytorch/language-modeling/
pip install -r requirements.txt
pip install transformers==v4.27.3
python3 run_clm.py \
--model_name_or_path gpt2-medium \
--train_file /home/egon/dev/gptsquad_data/converted_squad_merged_out_v4c.txt \
--do_train \
--num_train_epochs 3 \
--per_device_train_batch_size 1 \
--output_dir /home/egon/dev/gptsquad_model/results_v4c_gpt_medium_original_no_eval \
--fp16
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
sonny-dev/q-FrozenLake-v1-4x4-noSlippery
|
sonny-dev
| 2023-03-29T10:50:33Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T10:49:32Z |
---
tags:
- FrozenLake-v1-4x4
- 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
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.11 +/- 0.31
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="sonny-dev/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"])
```
|
miki030/q-FrozenLake-v1-4x4-noSlippery
|
miki030
| 2023-03-29T10:44:05Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T10:44:02Z |
---
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="miki030/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"])
```
|
yumingyi/a2c-PandaReachDense-v2
|
yumingyi
| 2023-03-29T10:37:12Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T08:35:02Z |
---
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: -0.38 +/- 0.18
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
...
```
|
vocabtrimmer/mt5-small-trimmed-ko-60000
|
vocabtrimmer
| 2023-03-29T10:35:39Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:50:48Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ko-60000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ko-60000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 105,503,104 |
| parameter_size_embedding | 256,114,688 | 61,441,024 |
| vocab_size | 250,112 | 60,001 |
| compression_rate_full | 100.0 | 35.15 |
| compression_rate_embedding | 100.0 | 23.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | 60000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ko-30000
|
vocabtrimmer
| 2023-03-29T10:32:19Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:45:41Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ko-30000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ko-30000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 74,783,104 |
| parameter_size_embedding | 256,114,688 | 30,721,024 |
| vocab_size | 250,112 | 30,001 |
| compression_rate_full | 100.0 | 24.91 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | 30000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ko-10000
|
vocabtrimmer
| 2023-03-29T10:29:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:39:19Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ko-10000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ko-10000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 54,303,104 |
| parameter_size_embedding | 256,114,688 | 10,241,024 |
| vocab_size | 250,112 | 10,001 |
| compression_rate_full | 100.0 | 18.09 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | 10000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ko-5000
|
vocabtrimmer
| 2023-03-29T10:28:22Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:36:53Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ko-5000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ko-5000 |
|:---------------------------|:-------------------|:-----------------------------------------|
| parameter_size_full | 300,176,768 | 49,183,104 |
| parameter_size_embedding | 256,114,688 | 5,121,024 |
| vocab_size | 250,112 | 5,001 |
| compression_rate_full | 100.0 | 16.38 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | 5000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-it-60000
|
vocabtrimmer
| 2023-03-29T10:24:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T13:33:24Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-it-60000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-it-60000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 105,503,104 |
| parameter_size_embedding | 256,114,688 | 61,441,024 |
| vocab_size | 250,112 | 60,001 |
| compression_rate_full | 100.0 | 35.15 |
| compression_rate_embedding | 100.0 | 23.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | 60000 | 2 |
|
Sandeep009/Sandy_Disease_Predection
|
Sandeep009
| 2023-03-29T10:22:11Z | 0 | 2 |
spacy
|
[
"spacy",
"medical",
"text-classification",
"en",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-03-29T09:10:40Z |
---
license: apache-2.0
language:
- en
library_name: spacy
pipeline_tag: text-classification
tags:
- medical
---
# 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:** Sandeep
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Type CLassification
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
|
vocabtrimmer/mt5-small-trimmed-it-30000
|
vocabtrimmer
| 2023-03-29T10:20:57Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T13:28:06Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-it-30000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-it-30000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 74,783,104 |
| parameter_size_embedding | 256,114,688 | 30,721,024 |
| vocab_size | 250,112 | 30,001 |
| compression_rate_full | 100.0 | 24.91 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | 30000 | 2 |
|
IndianServers/BhagavadGita
|
IndianServers
| 2023-03-29T10:19:10Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"pytorch",
"gpt2",
"question-answering",
"en",
"dataset:IndianServers/autotrain-data-bhagavat-gita-automl",
"license:apache-2.0",
"region:us"
] |
question-answering
| 2023-03-19T03:56:12Z |
---
license: apache-2.0
language:
- en
library_name: adapter-transformers
pipeline_tag: question-answering
datasets:
- IndianServers/autotrain-data-bhagavat-gita-automl
---
|
vocabtrimmer/mt5-small-trimmed-ru-120000
|
vocabtrimmer
| 2023-03-29T10:14:57Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T11:12:53Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ru-120000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ru-120000 |
|:---------------------------|:-------------------|:-------------------------------------------|
| parameter_size_full | 300,176,768 | 166,943,104 |
| parameter_size_embedding | 256,114,688 | 122,881,024 |
| vocab_size | 250,112 | 120,001 |
| compression_rate_full | 100.0 | 55.61 |
| compression_rate_embedding | 100.0 | 47.98 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 120000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ru-90000
|
vocabtrimmer
| 2023-03-29T10:11:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:27:45Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ru-90000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ru-90000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 136,223,104 |
| parameter_size_embedding | 256,114,688 | 92,161,024 |
| vocab_size | 250,112 | 90,001 |
| compression_rate_full | 100.0 | 45.38 |
| compression_rate_embedding | 100.0 | 35.98 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 90000 | 2 |
|
davidliu1110/bert-finetuned-ner
|
davidliu1110
| 2023-03-29T10:09:38Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-29T09:55:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9385688195251536
- name: Recall
type: recall
value: 0.9513631773813531
- name: F1
type: f1
value: 0.9449226911826161
- name: Accuracy
type: accuracy
value: 0.9864160828869135
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Precision: 0.9386
- Recall: 0.9514
- F1: 0.9449
- Accuracy: 0.9864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0874 | 1.0 | 1756 | 0.0683 | 0.9175 | 0.9334 | 0.9253 | 0.9821 |
| 0.0336 | 2.0 | 3512 | 0.0650 | 0.9310 | 0.9472 | 0.9390 | 0.9857 |
| 0.0169 | 3.0 | 5268 | 0.0617 | 0.9386 | 0.9514 | 0.9449 | 0.9864 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
ccoreilly/alpacat-lora-7b
|
ccoreilly
| 2023-03-29T10:00:43Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-03-29T09:55:18Z |
---
license: mit
---
This repo contains a low-rank adapter for LLaMA-7b
fit on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset.
It doesn't contain the foundation model itself, so it's MIT licensed!
The adapter was trained with a catalan translation of the cleaned alpaca dataset.
|
vocabtrimmer/mt5-small-trimmed-ru-5000
|
vocabtrimmer
| 2023-03-29T10:00:40Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T11:39:37Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ru-5000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ru-5000 |
|:---------------------------|:-------------------|:-----------------------------------------|
| parameter_size_full | 300,176,768 | 49,184,128 |
| parameter_size_embedding | 256,114,688 | 5,122,048 |
| vocab_size | 250,112 | 5,002 |
| compression_rate_full | 100.0 | 16.39 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ru | vocabtrimmer/mc4_validation | text | ru | validation | 5000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ja-90000
|
vocabtrimmer
| 2023-03-29T09:56:07Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:15:23Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ja-90000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ja-90000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 136,223,104 |
| parameter_size_embedding | 256,114,688 | 92,161,024 |
| vocab_size | 250,112 | 90,001 |
| compression_rate_full | 100.0 | 45.38 |
| compression_rate_embedding | 100.0 | 35.98 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 90000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ja-60000
|
vocabtrimmer
| 2023-03-29T09:53:33Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:31:35Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ja-60000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ja-60000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 105,503,104 |
| parameter_size_embedding | 256,114,688 | 61,441,024 |
| vocab_size | 250,112 | 60,001 |
| compression_rate_full | 100.0 | 35.15 |
| compression_rate_embedding | 100.0 | 23.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 60000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ja-15000
|
vocabtrimmer
| 2023-03-29T09:48:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:22:38Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ja-15000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ja-15000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 59,423,104 |
| parameter_size_embedding | 256,114,688 | 15,361,024 |
| vocab_size | 250,112 | 15,001 |
| compression_rate_full | 100.0 | 19.8 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 15000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ja-10000
|
vocabtrimmer
| 2023-03-29T09:47:15Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:19:43Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ja-10000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ja-10000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 54,303,104 |
| parameter_size_embedding | 256,114,688 | 10,241,024 |
| vocab_size | 250,112 | 10,001 |
| compression_rate_full | 100.0 | 18.09 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 10000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-ja-5000
|
vocabtrimmer
| 2023-03-29T09:46:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T10:17:18Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ja-5000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ja-5000 |
|:---------------------------|:-------------------|:-----------------------------------------|
| parameter_size_full | 300,176,768 | 49,183,104 |
| parameter_size_embedding | 256,114,688 | 5,121,024 |
| vocab_size | 250,112 | 5,001 |
| compression_rate_full | 100.0 | 16.38 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 5000 | 2 |
|
MarkieMark1/rl_course_vizdoom_health_gathering_supreme
|
MarkieMark1
| 2023-03-29T09:43:38Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T09:08:57Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.87 +/- 2.84
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MarkieMark1/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
vocabtrimmer/mt5-small-trimmed-fr-60000
|
vocabtrimmer
| 2023-03-29T09:39:00Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T12:25:16Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-fr-60000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-fr-60000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 105,503,104 |
| parameter_size_embedding | 256,114,688 | 61,441,024 |
| vocab_size | 250,112 | 60,001 |
| compression_rate_full | 100.0 | 35.15 |
| compression_rate_embedding | 100.0 | 23.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 60000 | 2 |
|
Bainbridge/gpt2-synth-real
|
Bainbridge
| 2023-03-29T09:37:52Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-29T09:16:01Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-synth-real
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-synth-real
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4267
## 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: 4
- seed: 21
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 80.0811 | 0.01 | 10 | 76.0005 |
| 63.2867 | 0.02 | 20 | 55.5177 |
| 30.7747 | 0.03 | 30 | 19.9807 |
| 17.8136 | 0.04 | 40 | 12.4432 |
| 6.5809 | 0.05 | 50 | 5.7868 |
| 3.8801 | 0.06 | 60 | 3.3238 |
| 2.2807 | 0.07 | 70 | 1.8328 |
| 1.3763 | 0.08 | 80 | 1.1114 |
| 0.8658 | 0.09 | 90 | 1.0464 |
| 0.748 | 0.1 | 100 | 0.8199 |
| 0.5694 | 0.12 | 110 | 0.5984 |
| 0.6427 | 0.13 | 120 | 0.6168 |
| 0.5534 | 0.14 | 130 | 0.5981 |
| 0.5483 | 0.15 | 140 | 0.5450 |
| 0.5384 | 0.16 | 150 | 0.4926 |
| 0.4926 | 0.17 | 160 | 0.4924 |
| 0.5059 | 0.18 | 170 | 0.4762 |
| 0.4372 | 0.19 | 180 | 0.4733 |
| 0.4833 | 0.2 | 190 | 0.4785 |
| 0.4511 | 0.21 | 200 | 0.4511 |
| 0.4163 | 0.22 | 210 | 0.4534 |
| 0.4849 | 0.23 | 220 | 0.4420 |
| 0.4857 | 0.24 | 230 | 0.4421 |
| 0.6163 | 0.25 | 240 | 0.4336 |
| 0.5151 | 0.26 | 250 | 0.4344 |
| 0.4533 | 0.27 | 260 | 0.4280 |
| 0.3812 | 0.28 | 270 | 0.4387 |
| 0.475 | 0.29 | 280 | 0.4274 |
| 0.4354 | 0.3 | 290 | 0.4272 |
| 0.3912 | 0.31 | 300 | 0.4305 |
| 0.3944 | 0.32 | 310 | 0.4204 |
| 0.4166 | 0.33 | 320 | 0.4239 |
| 0.3677 | 0.35 | 330 | 0.4246 |
| 0.4135 | 0.36 | 340 | 0.4267 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1
|
KanchiNiharika/biobert
|
KanchiNiharika
| 2023-03-29T09:37:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-03-29T09:31:58Z |
import pandas as pd
import re
import spacy
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score, classification_report
from sklearn.linear_model import LogisticRegression
# Load the data
data = pd.read_csv('symptomssingle.csv')
# Check for any missing values and remove them
data = data.dropna()
# Define a function to separate symptoms and diseases from the text
def separate_symptoms_and_diseases(text):
symptoms = re.findall(r'{"symptoms":"(.*?)"}', text)
disease = re.sub(r'(?:{"symptoms":".*?"},?)+', '', text).strip()
disease = disease.replace('],', '').strip() # Remove '],' from the disease name
return symptoms, disease
# Apply the function to the data
data['symptoms_and_diseases'] = data['data'].apply(separate_symptoms_and_diseases)
data[['symptoms', 'disease']] = pd.DataFrame(data['symptoms_and_diseases'].tolist(), index=data.index)
data = data.drop(columns=['data', 'symptoms_and_diseases'])
# Load the spaCy model
nlp = spacy.load('en_core_web_sm')
# Preprocessing function
def preprocess(symptoms):
processed_symptoms = []
for symptom in symptoms:
doc = nlp(symptom)
processed_symptom = ' '.join(token.lemma_.lower() for token in doc if not token.is_stop and token.is_alpha)
processed_symptoms.append(processed_symptom)
return ' '.join(processed_symptoms)
# Preprocess the symptoms column
data['symptoms_preprocessed'] = data['symptoms'].apply(preprocess)
# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(data['symptoms_preprocessed'], data['disease'], test_size=0.2, random_state=42)
# Create a pipeline for text classification
pipeline = Pipeline([
('tfidf', TfidfVectorizer(ngram_range=(1, 2))),
('classifier', LogisticRegression(solver='liblinear', C=10))
])
# Train the model
pipeline.fit(X_train, y_train)
# Make predictions
y_pred = pipeline.predict(X_test)
# Evaluate the model
print("Accuracy: ", accuracy_score(y_test, y_pred))
print("Classification Report:\n", classification_report(y_test, y_pred))
|
vocabtrimmer/mt5-small-trimmed-fr-30000
|
vocabtrimmer
| 2023-03-29T09:35:33Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T12:20:01Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-fr-30000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-fr-30000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 74,783,104 |
| parameter_size_embedding | 256,114,688 | 30,721,024 |
| vocab_size | 250,112 | 30,001 |
| compression_rate_full | 100.0 | 24.91 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 30000 | 2 |
|
migarbo1/metallica_lyric_gen
|
migarbo1
| 2023-03-29T09:32:21Z | 0 | 0 | null |
[
"text-generation",
"en",
"region:us"
] |
text-generation
| 2023-03-29T08:22:32Z |
---
language:
- en
pipeline_tag: text-generation
---
|
vocabtrimmer/mt5-small-trimmed-fr-5000
|
vocabtrimmer
| 2023-03-29T09:31:28Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T12:11:04Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-fr-5000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-fr-5000 |
|:---------------------------|:-------------------|:-----------------------------------------|
| parameter_size_full | 300,176,768 | 49,184,128 |
| parameter_size_embedding | 256,114,688 | 5,122,048 |
| vocab_size | 250,112 | 5,002 |
| compression_rate_full | 100.0 | 16.39 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 5000 | 2 |
|
vocabtrimmer/mt5-small-trimmed-es-120000
|
vocabtrimmer
| 2023-03-29T09:30:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T11:34:51Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-es-120000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-es-120000 |
|:---------------------------|:-------------------|:-------------------------------------------|
| parameter_size_full | 300,176,768 | 166,943,104 |
| parameter_size_embedding | 256,114,688 | 122,881,024 |
| vocab_size | 250,112 | 120,001 |
| compression_rate_full | 100.0 | 55.61 |
| compression_rate_embedding | 100.0 | 47.98 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 120000 | 2 |
|
cfalholt/Reinforce-Pixelcopter-PLE-v0
|
cfalholt
| 2023-03-29T09:23:49Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T09:23:40Z |
---
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: 50.40 +/- 43.40
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
|
AymenKallala/q-FrozenLake-v1-4x4-noSlippery
|
AymenKallala
| 2023-03-29T09:23:03Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T09:23:00Z |
---
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="AymenKallala/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"])
```
|
vocabtrimmer/mt5-small-trimmed-es-15000
|
vocabtrimmer
| 2023-03-29T09:18:35Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T12:59:27Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-es-15000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-es-15000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 59,423,104 |
| parameter_size_embedding | 256,114,688 | 15,361,024 |
| vocab_size | 250,112 | 15,001 |
| compression_rate_full | 100.0 | 19.8 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 15000 | 2 |
|
IndianServers/DiseasePredictionBasedOnSymptoms
|
IndianServers
| 2023-03-29T09:18:31Z | 0 | 1 |
spacy
|
[
"spacy",
"joblib",
"text-classification",
"en",
"dataset:IndianServers/diseasessymptoms",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-03-29T09:01:26Z |
---
license: apache-2.0
datasets:
- IndianServers/diseasessymptoms
language:
- en
pipeline_tag: text-classification
metrics:
- bertscore
library_name: spacy
---
|
vocabtrimmer/mt5-small-trimmed-es-5000
|
vocabtrimmer
| 2023-03-29T09:16:00Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-15T12:54:07Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-es-5000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-es-5000 |
|:---------------------------|:-------------------|:-----------------------------------------|
| parameter_size_full | 300,176,768 | 49,184,128 |
| parameter_size_embedding | 256,114,688 | 5,122,048 |
| vocab_size | 250,112 | 5,002 |
| compression_rate_full | 100.0 | 16.39 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 5000 | 2 |
|
cnicu/tweet_emotions_classifier
|
cnicu
| 2023-03-29T09:06:09Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-21T15:29:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
model-index:
- name: tweet_emotions_classifier
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: 0.7914438502673797
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Tweet Emotion Classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6042
- Accuracy: 0.7914
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8464 | 1.0 | 408 | 0.6180 | 0.7931 |
| 0.4583 | 2.0 | 816 | 0.5700 | 0.8037 |
### Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vocabtrimmer/mt5-small-trimmed-fr
|
vocabtrimmer
| 2023-03-29T08:59:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-04T18:17:09Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-fr`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-fr |
|:---------------------------|:-------------------|:------------------------------------|
| parameter_size_full | 300,176,768 | 178,294,144 |
| parameter_size_embedding | 256,114,688 | 134,232,064 |
| vocab_size | 250,112 | 131,086 |
| compression_rate_full | 100.0 | 59.4 |
| compression_rate_embedding | 100.0 | 52.41 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | | 2 |
|
huggingtweets/iusedtobeaduck
|
huggingtweets
| 2023-03-29T08:58:24Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-29T08:58:16Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1558973971810459649/glrTIMK-_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">stef</div>
<div style="text-align: center; font-size: 14px;">@iusedtobeaduck</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 stef.
| Data | stef |
| --- | --- |
| Tweets downloaded | 1412 |
| Retweets | 32 |
| Short tweets | 162 |
| Tweets kept | 1218 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/id8miur7/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 @iusedtobeaduck's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fjt95jfz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fjt95jfz/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/iusedtobeaduck')
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)
|
WilHoon/distilbert-base-uncased-finetuned-emotion
|
WilHoon
| 2023-03-29T08:51:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T07:56:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9264851417335438
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2217
- Accuracy: 0.9265
- F1: 0.9265
## 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8267 | 1.0 | 250 | 0.3277 | 0.9015 | 0.8977 |
| 0.2576 | 2.0 | 500 | 0.2217 | 0.9265 | 0.9265 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vocabtrimmer/mt5-small-trimmed-ko
|
vocabtrimmer
| 2023-03-29T08:40:53Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-04T16:58:21Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ko`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ko |
|:---------------------------|:-------------------|:------------------------------------|
| parameter_size_full | 300,176,768 | 119,178,624 |
| parameter_size_embedding | 256,114,688 | 75,116,544 |
| vocab_size | 250,112 | 73,356 |
| compression_rate_full | 100.0 | 39.7 |
| compression_rate_embedding | 100.0 | 29.33 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | | 2 |
|
Chetna19/m_albert_qa_model
|
Chetna19
| 2023-03-29T08:40:05Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:subjqa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-29T08:29:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- subjqa
model-index:
- name: m_albert_qa_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. -->
# m_albert_qa_model
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the subjqa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8510
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 32 | 2.6117 |
| No log | 2.0 | 64 | 2.5636 |
| No log | 3.0 | 96 | 2.6828 |
| No log | 4.0 | 128 | 2.8268 |
| No log | 5.0 | 160 | 2.8510 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vocabtrimmer/mt5-small-trimmed-ja
|
vocabtrimmer
| 2023-03-29T08:36:42Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-04T16:14:25Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-ja`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-ja |
|:---------------------------|:-------------------|:------------------------------------|
| parameter_size_full | 300,176,768 | 172,986,752 |
| parameter_size_embedding | 256,114,688 | 128,924,672 |
| vocab_size | 250,112 | 125,903 |
| compression_rate_full | 100.0 | 57.63 |
| compression_rate_embedding | 100.0 | 50.34 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | | 2 |
|
vocabtrimmer/mt5-small-trimmed-en-15000
|
vocabtrimmer
| 2023-03-29T08:30:50Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-29T08:29:53Z |
# Vocabulary Trimmed [google/mt5-small](https://huggingface.co/google/mt5-small): `vocabtrimmer/mt5-small-trimmed-en-15000`
This model is a trimmed version of [google/mt5-small](https://huggingface.co/google/mt5-small) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | google/mt5-small | vocabtrimmer/mt5-small-trimmed-en-15000 |
|:---------------------------|:-------------------|:------------------------------------------|
| parameter_size_full | 300,176,768 | 59,423,104 |
| parameter_size_embedding | 256,114,688 | 15,361,024 |
| vocab_size | 250,112 | 15,001 |
| compression_rate_full | 100.0 | 19.8 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| en | vocabtrimmer/mc4_validation | text | en | validation | 15000 | 2 |
|
april49/autotrain-t5-base-44767112714
|
april49
| 2023-03-29T08:15:49Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:april49/autotrain-data-t5-base",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-03-29T07:33:33Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- april49/autotrain-data-t5-base
co2_eq_emissions:
emissions: 15.98247816985612
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 44767112714
- CO2 Emissions (in grams): 15.9825
## Validation Metrics
- Loss: 0.856
- Rouge1: 28.704
- Rouge2: 6.275
- RougeL: 28.278
- RougeLsum: 28.253
- Gen Len: 57.661
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/april49/autotrain-t5-base-44767112714
```
|
huggingtweets/etherphoenix
|
huggingtweets
| 2023-03-29T08:13:18Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-29T08:13:08Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1599724651655364618/LZibszQx_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">Eric Bennett</div>
<div style="text-align: center; font-size: 14px;">@etherphoenix</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 Eric Bennett.
| Data | Eric Bennett |
| --- | --- |
| Tweets downloaded | 412 |
| Retweets | 29 |
| Short tweets | 30 |
| Tweets kept | 353 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/crz06ydh/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 @etherphoenix's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lync4yb1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lync4yb1/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/etherphoenix')
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)
|
reazon-research/reazonspeech-espnet-next
|
reazon-research
| 2023-03-29T07:35:13Z | 70 | 18 |
espnet
|
[
"espnet",
"automatic-speech-recognition",
"ja",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2023-03-29T07:20:03Z |
---
license: apache-2.0
language:
- ja
library_name: espnet
tags:
- automatic-speech-recognition
---
# reazonspeech-espnet-next
ReazonSpeech is a project to maintain freely-available Japanese audio
datasets and ML models.
`reazonspeech-espnet-next` is a "bleeding-edge" repository that contains
latest ASR models trained by ReazonSpeech team.
We maintain this repository because we want to make our latest research
results readily available, and try to incorporate feedback from community
as quickly as possible.
If you are curious about the project, or have built something based on
ReazonSpeech model, please let us know!
Project site: https://research.reazon.jp/projects/ReazonSpeech/index.html
|
DFKI-SLT/eurogpt2
|
DFKI-SLT
| 2023-03-29T07:27:52Z | 0 | 6 | null |
[
"bg",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"ga",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"pl",
"pt",
"ro",
"sk",
"sl",
"sv",
"uk",
"multilingual",
"arxiv:2104.09864",
"license:mit",
"region:us"
] | null | 2023-03-23T10:07:44Z |
---
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
- uk
- multilingual
license: mit
---
# EuroGPT2
**NOTE: THIS IS THE ORIGINAL MEGATRON-DEEPSPEED CHECKPOINT INCLUDING OPTIMIZER STATES**
A GPT2 language model for European languages (EU-24 + Ukrainian).
The model follows the original architecture as [OpenAI's GPT2](https://huggingface.co/gpt2/) apart from using [rotary](https://arxiv.org/abs/2104.09864) instead of learned positional embeddigs.
## Model settings
- parameters: 124M
- number of layers: 12
- hidden size: 768
- number of heads: 12
- sequence length: 1024
- batch size: 168
- test PPL after training: 23.6 (steps: 436,940)
## Training data
- [Wikimedia dumps](https://dumps.wikimedia.org/) (Wikipedia, Wikinews, Wikibooks, Wikisource, Wikivoyage; 20230301)
- [EUR-Lex](https://huggingface.co/datasets/joelito/eurlex_resources)
- [OSCAR 2023.01](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301)
- Tokens: 75,167,662,080
## Languages
Included languages: Bulgarian, Czech, Danish, German, Greek, English, Spanish, Estonian, Finnish, French, Irish, Croatian, Hungarian, Italian, Lithuanian, Latvian, Maltese, Dutch, Polish, Portuguese, Romanian, Slovak, Slovenian, Swedish, and Ukrainian.
| Language | Ratio |
| -------- | ------ |
| bg | 5,92% |
| cs | 4,77% |
| da | 2,19% |
| de | 7,36% |
| el | 8,60% |
| en | 10,11% |
| es | 6,57% |
| et | 1,67% |
| fi | 2,70% |
| fr | 7,18% |
| ga | 0,25% |
| hr | 1,09% |
| hu | 6,38% |
| it | 5,80% |
| lt | 2,01% |
| lv | 1,76% |
| mt | 1,49% |
| nl | 5,20% |
| pl | 4,82% |
| pt | 4,64% |
| ro | 2,93% |
| sk | 2,03% |
| sl | 1,54% |
| sv | 3,00% |
## License
MIT
|
sohamchougule/t5-small-finetuned-samsum
|
sohamchougule
| 2023-03-29T07:24:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-03-28T10:17:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 42.3964
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-samsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7384
- Rouge1: 42.3964
- Rouge2: 19.5954
- Rougel: 35.9558
- Rougelsum: 39.5162
- Gen Len: 16.6883
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9071 | 1.0 | 1842 | 1.7384 | 42.3964 | 19.5954 | 35.9558 | 39.5162 | 16.6883 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Meohong/distilbert-base-uncased-finetuned-imdb
|
Meohong
| 2023-03-29T07:20:30Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T07:08:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4497
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 79 | 2.5252 |
| 2.667 | 2.0 | 158 | 2.4665 |
| 2.667 | 3.0 | 237 | 2.4468 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Chetna19/m_roberta_qa_model
|
Chetna19
| 2023-03-29T07:18:46Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:subjqa",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-03-29T07:12:50Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- subjqa
model-index:
- name: m_roberta_qa_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. -->
# m_roberta_qa_model
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the subjqa dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1134
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 32 | 2.6082 |
| No log | 2.0 | 64 | 2.6972 |
| No log | 3.0 | 96 | 2.9758 |
| No log | 4.0 | 128 | 3.0284 |
| No log | 5.0 | 160 | 3.1134 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Galeros/ppo-CartPole-v1
|
Galeros
| 2023-03-29T07:00:42Z | 0 | 0 | null |
[
"tensorboard",
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-09T09:07:05Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: ppo-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
|
terry918/sssss
|
terry918
| 2023-03-29T07:00:20Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-03-29T07:00:20Z |
---
license: bigscience-openrail-m
---
|
fuchuan/fczsy
|
fuchuan
| 2023-03-29T06:56:44Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-03-26T09:37:54Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
|
alphaSaber/bert-finetuned-ner
|
alphaSaber
| 2023-03-29T06:22:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-03-28T16:14:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9375621066578337
- name: Recall
type: recall
value: 0.9527095254123191
- name: F1
type: f1
value: 0.9450751252086811
- name: Accuracy
type: accuracy
value: 0.9867251427562254
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.9376
- Recall: 0.9527
- F1: 0.9451
- Accuracy: 0.9867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0858 | 1.0 | 1756 | 0.0682 | 0.9096 | 0.9330 | 0.9212 | 0.9817 |
| 0.0387 | 2.0 | 3512 | 0.0609 | 0.9310 | 0.9487 | 0.9397 | 0.9859 |
| 0.0209 | 3.0 | 5268 | 0.0606 | 0.9376 | 0.9527 | 0.9451 | 0.9867 |
### Framework versions
- Transformers 4.27.1
- Pytorch 1.10.2+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2
|
edkesuma/ppo-LunarLander-v2
|
edkesuma
| 2023-03-29T06:03:27Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T05:50:23Z |
---
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: 284.17 +/- 23.20
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
...
```
|
tartuNLP/mtee-domain-detection
|
tartuNLP
| 2023-03-29T05:59:10Z | 114 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"et",
"en",
"ru",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-26T13:25:31Z |
---
language:
- et
- en
- ru
- de
tags:
- text-classification
widget:
- text: "Täna lõppes Valgamaa õppuse Siil aktiivne lahingutegevus, mille käigus pidi täielikult formeeritud 2. jalaväebrigaad kaitsma end vastase pealetungi eest."
---
A domain detection model for the MTee machine translation platform. The platform was developed in 2021 as a collaboration between the [TartuNLP](https://tartunlp.ai), the NLP research group at the University of Tartu, and [Tilde](https://tilde.com). More information about the project can be found [here](https://github.com/Project-MTee/mtee-platform/wiki).
#### Model Description
The model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base). It classifies the input sentence into one of the following four domains: `general`, `crisis`, `legal`, `military`.
|
leejw51/vit-base-beans
|
leejw51
| 2023-03-29T05:51:47Z | 233 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"vision",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-03-17T06:59:51Z |
---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9849624060150376
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0875
- Accuracy: 0.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.2574 | 1.0 | 130 | 0.9624 | 0.2307 |
| 0.2785 | 2.0 | 260 | 0.9925 | 0.1109 |
| 0.1496 | 3.0 | 390 | 0.9699 | 0.1109 |
| 0.0916 | 4.0 | 520 | 0.9850 | 0.0875 |
| 0.1489 | 5.0 | 650 | 0.9774 | 0.0886 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
FourthBrainGenAI/ProductSnapAI
|
FourthBrainGenAI
| 2023-03-29T05:40:59Z | 49 | 4 |
diffusers
|
[
"diffusers",
"tensorboard",
"art",
"text-to-image",
"en",
"dataset:Ali-fb/martin_valen_dataset",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-29T05:25:21Z |
---
license: openrail
datasets:
- Ali-fb/martin_valen_dataset
language:
- en
metrics:
- accuracy
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
---
|
akadhim-ai/sd_hoodies-model-v1-2_400
|
akadhim-ai
| 2023-03-29T05:08:01Z | 30 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"art",
"text-to-image",
"en",
"dataset:Ali-fb/hoodies_dataset",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-29T04:49:32Z |
---
license: openrail
datasets:
- Ali-fb/hoodies_dataset
language:
- en
metrics:
- accuracy
library_name: diffusers
pipeline_tag: text-to-image
tags:
- art
---
|
meghtedari/whisper-base
|
meghtedari
| 2023-03-29T04:56:46Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-03-29T01:06:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: meghtedariatc
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. -->
# meghtedariatc
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1820
- Wer: 53.4928
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0002 | 76.92 | 1000 | 1.1820 | 53.4928 |
| 0.0002 | 153.85 | 2000 | 1.1820 | 53.4928 |
| 0.0002 | 230.77 | 3000 | 1.1820 | 53.4928 |
| 0.0002 | 307.69 | 4000 | 1.1820 | 53.4928 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
vsevolodl/pokemon-lora
|
vsevolodl
| 2023-03-29T04:44:26Z | 0 | 2 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-29T01:29:55Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/vsevolodl/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
kejian/cpsc-log5-bin4-5repeat-v2
|
kejian
| 2023-03-29T04:23:54Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"generated_from_trainer",
"en",
"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",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-03-28T05:45:39Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- 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
model-index:
- name: kejian/cpsc-log5-bin4-5repeat-v2
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. -->
# kejian/cpsc-log5-bin4-5repeat-v2
This model was trained from scratch on 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 and the tomekkorbak/detoxify-pile-chunk3-1800000-1850000 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 42724
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.23.0
- Pytorch 1.13.0+cu116
- Datasets 2.0.0
- Tokenizers 0.12.1
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>',
'drop_token_fraction': 0.02,
'fine_prefix': '<|fine|><|fine|><|fine|><|fine|><|fine|>',
'misaligned_prefix': '<|misaligned|><|misaligned|><|misaligned|><|misaligned|><|misaligned|>',
'substandard_prefix': '<|substandard|><|substandard|><|substandard|><|substandard|><|substandard|>',
'threshold1': 0.0005623,
'threshold2': 0.0005842,
'threshold3': 0.0006632,
'threshold4': 0.9992},
'datasets': ['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'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [21362],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-fine',
'num_samples': 1024,
'prefix': '<|fine|><|fine|><|fine|><|fine|><|fine|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-substandard',
'num_samples': 1024,
'prefix': '<|substandard|><|substandard|><|substandard|><|substandard|><|substandard|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional-misaligned',
'num_samples': 1024,
'prefix': '<|misaligned|><|misaligned|><|misaligned|><|misaligned|><|misaligned|>'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp',
'num_samples': 1024,
'prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'},
{'generate_kwargs': {'bad_words_ids': [[50257],
[50258],
[50259],
[50260]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'challenging_rtp-bad-control',
'num_samples': 1024,
'prefix': '<|misaligned|><|misaligned|><|misaligned|><|misaligned|><|misaligned|>',
'prompt_before_control': True,
'prompts_path': 'resources/challenging_rtp.jsonl'}],
'scorer_config': {'device': 'cuda:0'}},
'kl_gpt3_callback': {'force_call_on': [21362],
'gpt3_kwargs': {'model_name': 'davinci'},
'max_tokens': 64,
'num_samples': 2048,
'prefix': '<|aligned|><|aligned|><|aligned|><|aligned|><|aligned|>',
'should_insert_prefix': True},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 4,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>',
'<|fine|>',
'<|substandard|>',
'<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'kejian/cpsc-log5-bin4-5repeat-v2',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 50,
'num_tokens': 2800000000.0,
'output_dir': 'training_output_99-v2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 21362,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/kejian/uncategorized/runs/21m33m2k
|
Niraya666/dqn-SpaceInvadersNoFrameskip-v4_8
|
Niraya666
| 2023-03-29T04:06:37Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T04:03:48Z |
---
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: 554.50 +/- 116.07
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 Niraya666 -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 Niraya666 -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 Niraya666
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 50000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huggingtweets/hutaosoulmate
|
huggingtweets
| 2023-03-29T03:49:40Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-29T03:43:25Z |
---
language: en
thumbnail: http://www.huggingtweets.com/hutaosoulmate/1680061774875/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/1539749115092934656/WeP6cOjo_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">Laurent</div>
<div style="text-align: center; font-size: 14px;">@hutaosoulmate</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 Laurent.
| Data | Laurent |
| --- | --- |
| Tweets downloaded | 1181 |
| Retweets | 212 |
| Short tweets | 101 |
| Tweets kept | 868 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/y718bopk/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 @hutaosoulmate's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/vz2s932i) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/vz2s932i/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/hutaosoulmate')
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)
|
huggingtweets/sansansansaname
|
huggingtweets
| 2023-03-29T03:32:07Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-29T03:31:56Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1636311878275063814/mAnmCXzQ_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">Ai-oh 💫</div>
<div style="text-align: center; font-size: 14px;">@sansansansaname</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 Ai-oh 💫.
| Data | Ai-oh 💫 |
| --- | --- |
| Tweets downloaded | 1999 |
| Retweets | 24 |
| Short tweets | 1306 |
| Tweets kept | 669 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/c2qx733w/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 @sansansansaname's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5b93euwo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5b93euwo/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/sansansansaname')
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)
|
davidliu1110/bert-fine-tuned-cola
|
davidliu1110
| 2023-03-29T03:31:43Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-03-29T03:01:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: bert-fine-tuned-cola
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-fine-tuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8369
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 459 | 0.4187 |
| 0.5148 | 2.0 | 918 | 0.5389 |
| 0.3202 | 3.0 | 1377 | 0.6432 |
| 0.1684 | 4.0 | 1836 | 0.7600 |
| 0.101 | 5.0 | 2295 | 0.8369 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
hfl/chinese-alpaca-lora-7b
|
hfl
| 2023-03-29T03:12:13Z | 0 | 67 | null |
[
"zh",
"license:apache-2.0",
"region:us"
] | null | 2023-03-29T02:04:13Z |
---
license: apache-2.0
language:
- zh
---
# Chinese-Alpaca-LoRA-7B
This repo contains the tokenizer, Chinese-Alpaca LoRA weights and configs for [Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
Instructions for using the weights can be found at https://github.com/ymcui/Chinese-LLaMA-Alpaca.
|
serkanBurakOrs/a2c-AntBulletEnv-v0
|
serkanBurakOrs
| 2023-03-29T02:45:47Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-29T02:44:33Z |
---
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: 1248.89 +/- 21.54
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
...
```
|
kikijiki/a2c-AntBulletEnv-v0
|
kikijiki
| 2023-03-29T02:38:26Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-03-26T14:51:24Z |
---
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: 2109.35 +/- 55.69
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
...
```
|
GraydientPlatformAPI/model_112
|
GraydientPlatformAPI
| 2023-03-29T01:50:34Z | 30 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-03-28T13:54:08Z |
---
license: openrail
library_name: diffusers
pipeline_tag: text-to-image
---
|
huggingtweets/ottorothmund
|
huggingtweets
| 2023-03-29T01:48:38Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-29T02:16:58Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1640242374264729600/egMDKzZ6_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">OTTO ☀︎</div>
<div style="text-align: center; font-size: 14px;">@ottorothmund</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 OTTO ☀︎.
| Data | OTTO ☀︎ |
| --- | --- |
| Tweets downloaded | 3180 |
| Retweets | 515 |
| Short tweets | 672 |
| Tweets kept | 1993 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/s0kjsfe0/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 @ottorothmund's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3m35juvv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3m35juvv/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/ottorothmund')
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)
|
huggingtweets/quietluke
|
huggingtweets
| 2023-03-29T01:45:09Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-29T01:45:00Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1632674116112089088/wjGdtuux_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">quiet luke</div>
<div style="text-align: center; font-size: 14px;">@quietluke</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 quiet luke.
| Data | quiet luke |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 211 |
| Short tweets | 1074 |
| Tweets kept | 1959 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/44qcjzz1/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 @quietluke's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x3gisyhj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x3gisyhj/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/quietluke')
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)
|
vocabtrimmer/xlm-roberta-base-trimmed-ar-30000
|
vocabtrimmer
| 2023-03-29T01:34:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T01:33:05Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-ar-30000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-ar-30000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 109,115,186 |
| parameter_size_embedding | 192,001,536 | 23,041,536 |
| vocab_size | 250,002 | 30,002 |
| compression_rate_full | 100.0 | 39.21 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ar | vocabtrimmer/mc4_validation | text | ar | validation | 30000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-ar-15000
|
vocabtrimmer
| 2023-03-29T01:31:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T01:30:44Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-ar-15000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-ar-15000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 97,580,186 |
| parameter_size_embedding | 192,001,536 | 11,521,536 |
| vocab_size | 250,002 | 15,002 |
| compression_rate_full | 100.0 | 35.06 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ar | vocabtrimmer/mc4_validation | text | ar | validation | 15000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-ar-10000
|
vocabtrimmer
| 2023-03-29T01:29:58Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T01:28:57Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-ar-10000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-ar-10000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 93,735,186 |
| parameter_size_embedding | 192,001,536 | 7,681,536 |
| vocab_size | 250,002 | 10,002 |
| compression_rate_full | 100.0 | 33.68 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ar | vocabtrimmer/mc4_validation | text | ar | validation | 10000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-ar
|
vocabtrimmer
| 2023-03-29T01:26:57Z | 154 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T01:25:37Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-ar`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-ar |
|:---------------------------|:-------------------|:-------------------------------------------|
| parameter_size_full | 278,295,186 | 124,394,447 |
| parameter_size_embedding | 192,001,536 | 38,300,928 |
| vocab_size | 250,002 | 49,871 |
| compression_rate_full | 100.0 | 44.7 |
| compression_rate_embedding | 100.0 | 19.95 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| ar | vocabtrimmer/mc4_validation | text | ar | validation | | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-de-60000
|
vocabtrimmer
| 2023-03-29T01:22:02Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T01:20:38Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-de-60000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-de-60000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 132,185,186 |
| parameter_size_embedding | 192,001,536 | 46,081,536 |
| vocab_size | 250,002 | 60,002 |
| compression_rate_full | 100.0 | 47.5 |
| compression_rate_embedding | 100.0 | 24.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| de | vocabtrimmer/mc4_validation | text | de | validation | 60000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-de-15000
|
vocabtrimmer
| 2023-03-29T01:15:42Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T01:14:32Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-de-15000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-de-15000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 97,580,186 |
| parameter_size_embedding | 192,001,536 | 11,521,536 |
| vocab_size | 250,002 | 15,002 |
| compression_rate_full | 100.0 | 35.06 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| de | vocabtrimmer/mc4_validation | text | de | validation | 15000 | 2 |
|
jfacevedo/alpaca-gptj
|
jfacevedo
| 2023-03-29T01:13:17Z | 0 | 6 |
transformers
|
[
"transformers",
"chat",
"chat-gptj",
"gptj",
"en",
"dataset:yahma/alpaca-cleaned",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-03-28T02:20:41Z |
---
license: apache-2.0
datasets:
- yahma/alpaca-cleaned
language:
- en
library_name: transformers
tags:
- chat
- chat-gptj
- gptj
---
# Instruction Tuned GPTJ LoRA using the Alpaca Dataset
The repo with all the training code can be found at https://github.com/entrpn/serving-model-cards/tree/main/instruct-tune-gptj
This model contains the LoRA weights for GPTJ-6B. The model was fine tuned on a T4 GPU using 8-bit quantization, gradient checkpointing and gradient accumulation.
This was trained in a Google Cloud Platform Compute Engine spot VM for 3k steps, costing less than $2 dollars.
The license should follow the same as [Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html). However, you should be able to use this method with your own dataset and not have the same restrictions.
Also want to shout out to @tloen as I used his some of his code to generate the prompts for training and inference. Please check out the author's repo https://github.com/tloen/alpaca-lora
## Generations
First let's look at some results
```text
Instruction : How can I make friends?
Response : The best way to make friends is to be friendly and approachable. Show interest in other people by asking them about their lives, interests, and hobbies. Make an effort to get to know people better by inviting them to social events and inviting them to join you in activities. Make an effort to get to know people better by asking them about their lives, interests, and hobbies. Show interest in other people by asking them about their lives, interests, and hobbies. Make an effort to get to know people better by inviting them to social events and inviting them to join you in activities. Make an effort to get to know people better by
```
```text
Instruction : Generate an example of what a resume should like for an engineering professional
Response : An example of a resume for an engineering professional should include the following information: name, contact information, education, professional experience, skills, and references. The education section should include a list of degrees, certifications, and professional experience. The professional experience section should include a list of projects, responsibilities, and accomplishments. The skills section should include a list of technical and non-technical skills. Finally, the references section should include a list of professional references.
```
```text
Instruction : Tell me about lions.
Response : Lions are one of the largest cats in the world. They are found in Africa, Asia, and parts of the Middle East. They are known for their powerful jaws, sharp claws, and large manes. They are also known for their ability to roar, which can be heard up to a mile away. They are also known for their ability to hunt and kill large prey such as antelopes, zebras, and wildebeests. They are also known for their ability to defend their territory and their cubs. They are also known for their ability to roar, which can be heard up to a mile away
```
```text
Instruction : What are alcapas? How are alpacas different from llamas?
Response : Alpacas and llamas are both members of the camelid family. Alpacas are smaller than llamas and have shorter legs. Alpacas are native to South America, while llamas are native to the Andes Mountains of South America. Alpacas and llamas are similar in appearance, but alpacas are smaller and have shorter legs than llamas. Alpacas and llamas are both used as pack animals, but alpacas are smaller and have shorter legs than llamas. Alpacas and llamas are both used for milk production, but alpacas are smaller and have shorter legs than
```
```text
Instruction: How can a human become smarter?
Response: A human can become smarter by learning new skills and knowledge. Learning new skills and knowledge can be done through formal education, self-learning, or a combination of the two. Formal education can be done through school, college, or university. Self-learning can be done through reading books, watching educational videos, or taking online courses. Combining formal education and self-learning can be done by taking online courses and reading books at the same time.\n\n### Instruction: How can a human become more intelligent?\n
```
## Examples
Install dependencies.
```shell
conda create -n py39 python=3.9
conda activate py39
pip install transformers tensorboard accelerate datasets torch bitsandbytes git+https://github.com/huggingface/peft.git
```
To generate, use the following
```python
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
def generate_prompt(instruction, input=None):
if input:
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
else:
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""
def evaluate(
model,
tokenizer,
instruction,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
max_new_tokens=128,
**kwargs,
):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to("cuda")
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output#.sequences[0]
s = s.sequences[0]
output = tokenizer.decode(s)
return output.split("### Response:")[1].strip()
peft_model_id = "jfacevedo/gptj-alpaca"
model_name = "EleutherAI/gpt-j-6B"
config = PeftConfig.from_pretrained(peft_model_id)
print("loading model")
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto",revision="float16", load_in_8bit=True)
print("loading tokenizer")
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Padding token should not be required for inference, but adding it since it was added during training
# Add pad token
new_tokens = [""]
# check if the tokens are already in the vocabulary
new_tokens = set(new_tokens) - set(tokenizer.vocab.keys())
# add the tokens to the tokenizer vocabulary
tokenizer.add_tokens(list(new_tokens))
# add new, random embeddings for the new tokens
model.resize_token_embeddings(len(tokenizer))
tokenizer.pad_token = ""
# Load the Lora model
instruction = "Describe the structure of an atom."
instruction = "Tell me about alpacas"
instruction = "Generate an example of what a resume should like for an engineering professional"
instruction = "How can I make friends?"
print("instruction: ",instruction)
# Testing the original model
# print("Not finetuned")
# print("Response:", evaluate(model, tokenizer, instruction))
# print("\n\n")
print("loading lora model")
model = PeftModel.from_pretrained(model, peft_model_id).to("cuda")
print("Finetuned model")
print("Response:", evaluate(model, tokenizer, instruction))
```
|
vocabtrimmer/xlm-roberta-base-trimmed-de
|
vocabtrimmer
| 2023-03-29T01:10:42Z | 151 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T01:09:04Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-de`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-de |
|:---------------------------|:-------------------|:-------------------------------------------|
| parameter_size_full | 278,295,186 | 156,557,872 |
| parameter_size_embedding | 192,001,536 | 70,422,528 |
| vocab_size | 250,002 | 91,696 |
| compression_rate_full | 100.0 | 56.26 |
| compression_rate_embedding | 100.0 | 36.68 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|:--------------------|----------------:|
| de | vocabtrimmer/mc4_validation | text | de | validation | | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-es-15000
|
vocabtrimmer
| 2023-03-29T00:48:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T00:47:38Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-es-15000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-es-15000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 97,580,186 |
| parameter_size_embedding | 192,001,536 | 11,521,536 |
| vocab_size | 250,002 | 15,002 |
| compression_rate_full | 100.0 | 35.06 |
| compression_rate_embedding | 100.0 | 6.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 15000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-es-10000
|
vocabtrimmer
| 2023-03-29T00:47:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T00:46:02Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-es-10000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-es-10000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 93,735,186 |
| parameter_size_embedding | 192,001,536 | 7,681,536 |
| vocab_size | 250,002 | 10,002 |
| compression_rate_full | 100.0 | 33.68 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 10000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-es-5000
|
vocabtrimmer
| 2023-03-29T00:45:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T00:44:35Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-es-5000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-es-5000 |
|:---------------------------|:-------------------|:------------------------------------------------|
| parameter_size_full | 278,295,186 | 89,890,186 |
| parameter_size_embedding | 192,001,536 | 3,841,536 |
| vocab_size | 250,002 | 5,002 |
| compression_rate_full | 100.0 | 32.3 |
| compression_rate_embedding | 100.0 | 2.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 5000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-it-30000
|
vocabtrimmer
| 2023-03-29T00:37:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T00:36:30Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-it-30000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-it-30000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 109,115,186 |
| parameter_size_embedding | 192,001,536 | 23,041,536 |
| vocab_size | 250,002 | 30,002 |
| compression_rate_full | 100.0 | 39.21 |
| compression_rate_embedding | 100.0 | 12.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | 30000 | 2 |
|
vocabtrimmer/xlm-roberta-base-trimmed-it-10000
|
vocabtrimmer
| 2023-03-29T00:33:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-03-29T00:32:54Z |
# Vocabulary Trimmed [xlm-roberta-base](https://huggingface.co/xlm-roberta-base): `vocabtrimmer/xlm-roberta-base-trimmed-it-10000`
This model is a trimmed version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | xlm-roberta-base | vocabtrimmer/xlm-roberta-base-trimmed-it-10000 |
|:---------------------------|:-------------------|:-------------------------------------------------|
| parameter_size_full | 278,295,186 | 93,735,186 |
| parameter_size_embedding | 192,001,536 | 7,681,536 |
| vocab_size | 250,002 | 10,002 |
| compression_rate_full | 100.0 | 33.68 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | 10000 | 2 |
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.