modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Contrastive-Tension/BERT-Base-CT-STSb
|
[
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 5 | null |
Access to model ihsass/Sami is restricted and you are not in the authorized list. Visit https://huggingface.co/ihsass/Sami to ask for access.
|
Contrastive-Tension/BERT-Base-NLI-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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| 9 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### dating_avatar_model Dreambooth model trained by sagu7 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Contrastive-Tension/BERT-Distil-CT-STSb
|
[
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 1 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Prgrg/en-ja-v2.0
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Prgrg/en-ja-v2.0
This model is a fine-tuned version of [Prgrg/en-ja-v1.0](https://huggingface.co/Prgrg/en-ja-v1.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.3630
- Validation Loss: 1.2679
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 32426, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.7452 | 1.5073 | 0 |
| 1.3630 | 1.2679 | 1 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Contrastive-Tension/BERT-Distil-NLI-CT
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"DistilBertForMaskedLM"
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| 6 | null |
Access to model bigcode/starencoder is restricted and you are not in the authorized list. Visit https://huggingface.co/bigcode/starencoder to ask for access.
|
Crasher222/kaggle-comp-test
|
[
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
] |
text-classification
|
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| 29 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 301.50 +/- 68.67
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 nasheed -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 nasheed -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 nasheed
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
CrayonShinchan/fine_tune_try_1
|
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| 0 | null |
---
language:
- et
---
Jamspell model from etnc19 reference corpus data
|
Crisblair/Wkwk
|
[] | null |
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| 0 | null |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: I love AutoTrain 🤗
datasets:
- cnn_dailymail
co2_eq_emissions:
emissions: 0.006114084839310591
metrics:
- bleu
pipeline_tag: summarization
---
# Model Trained Using AutoTrain
- Trained from FLAN-T5 large
- Problem type: Summarization
- Model ID: 40818105603
- CO2 Emissions (in grams): 0.0061
## Validation Metrics
- Loss: 0.055
- Rouge1: 44.548
- Rouge2: 42.697
- RougeL: 44.530
- RougeLsum: 44.567
- Gen Len: 19.000
## 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/aszfcxcgszdx/autotrain-summary2.0-40818105603
```
|
Crispy/dialopt-small-kratos
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-bbc
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. -->
# distilroberta-base-finetuned-bbc
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7691
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 21 | 2.8306 |
| No log | 2.0 | 42 | 2.5844 |
| No log | 3.0 | 63 | 2.7691 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Cthyllax/DialoGPT-medium-PaladinDanse
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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| 10 | null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- davanstrien/autotrain-data-mapreader
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.3861823058569251
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 40821105604
- CO2 Emissions (in grams): 0.3862
## Validation Metrics
- Loss: 0.147
- Accuracy: 0.955
- Macro F1: 0.737
- Micro F1: 0.955
- Weighted F1: 0.953
- Macro Precision: 0.842
- Micro Precision: 0.955
- Weighted Precision: 0.954
- Macro Recall: 0.679
- Micro Recall: 0.955
- Weighted Recall: 0.955
|
Culmenus/IceBERT-finetuned-ner
|
[
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:gpl-3.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
{
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"RobertaForTokenClassification"
],
"model_type": "roberta",
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| 5 | null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- davanstrien/autotrain-data-mapreader
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.003656437426363874
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 40821105606
- CO2 Emissions (in grams): 0.0037
## Validation Metrics
- Loss: 0.437
- Accuracy: 0.906
- Macro F1: 0.238
- Micro F1: 0.906
- Weighted F1: 0.861
- Macro Precision: 0.226
- Micro Precision: 0.906
- Weighted Precision: 0.821
- Macro Recall: 0.250
- Micro Recall: 0.906
- Weighted Recall: 0.906
|
CurtisBowser/DialoGPT-medium-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-bbc_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-bbc_2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5300
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 21 | 2.8306 |
| No log | 2.0 | 42 | 2.5844 |
| No log | 3.0 | 63 | 2.7691 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
CurtisBowser/DialoGPT-small-sora
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
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},
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"translation_en_to_fr": {
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}
}
}
| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: dgx1_w2v2_base_finetune_teacher_babble_noise_libri_360_hours_50_epochs_batch_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dgx1_w2v2_base_finetune_teacher_babble_noise_libri_360_hours_50_epochs_batch_4
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 48.7078
- Wer: 0.2642
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 484.5509 | 0.02 | 500 | 28.4641 | 0.1563 |
| 293.5338 | 0.04 | 1000 | 25.8292 | 0.1446 |
| 293.9193 | 0.06 | 1500 | 29.0716 | 0.1710 |
| 322.8119 | 0.08 | 2000 | 34.0894 | 0.1971 |
| 365.3664 | 0.1 | 2500 | 36.7096 | 0.2180 |
| 404.8819 | 0.12 | 3000 | 41.7040 | 0.2413 |
| 435.5897 | 0.13 | 3500 | 48.7078 | 0.2642 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
D3xter1922/distilbert-base-uncased-finetuned-cola
|
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| 0 | null |
---
tags:
- generated_from_trainer
datasets:
- city_learn
model-index:
- name: decision_transformer_rb_461
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. -->
# decision_transformer_rb_461
This model is a fine-tuned version of [](https://huggingface.co/) on the city_learn dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- 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_ratio: 0.1
- num_epochs: 75
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DCU-NLP/bert-base-irish-cased-v1
|
[
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
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}
| 1,244 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-railspace
results: []
widget:
- src: https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/1.png
example_title: patch
- src: https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/271.png
example_title: patch
---
<!-- 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-demo-v5
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0292
- Accuracy: 0.9926
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
precision recall f1-score support
0 1.00 1.00 1.00 11315
1 0.92 0.94 0.93 204
2 0.95 0.97 0.96 714
3 0.87 0.98 0.92 171
macro avg 0.93 0.97 0.95 12404
weighted avg 0.99 0.99 0.99 12404
accuracy 0.99 12404
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0206 | 1.72 | 1000 | 0.0422 | 0.9854 |
| 0.0008 | 3.44 | 2000 | 0.0316 | 0.9918 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
alexandrainst/da-hatespeech-detection-base
|
[
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 1,719 | 2023-03-13T17:38:53Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- aszfcxcgszdx/autotrain-data-summary-v3
co2_eq_emissions:
emissions: 3.520254114566687
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 40835105619
- CO2 Emissions (in grams): 3.5203
## Validation Metrics
- Loss: 1.818
- Rouge1: 44.176
- Rouge2: 25.696
- RougeL: 41.172
- RougeLsum: 41.276
- Gen Len: 15.201
## 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/aszfcxcgszdx/autotrain-summary-v3-40835105619
```
|
Daltcamalea01/Camaleaodalt
|
[] | null |
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}
| 0 | 2023-03-13T17:57:19Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi_v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 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="peterdamn/taxi_v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DataikuNLP/paraphrase-MiniLM-L6-v2
|
[
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
| 25 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="henrikho/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"])
```
|
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2
|
[
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] |
sentence-similarity
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 1,517 | 2023-03-13T19:05:58Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="henrikho/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DavidAMcIntosh/DialoGPT-small-rick
|
[] | null |
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}
}
| 0 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: umit_7allV3_full
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. -->
# umit_7allV3_full
Dataset:
https://www.kaggle.com/datasets/savasy/ttc4900
0:siyaset 1:dünya 2:ekonomi 3:kültür 4:saglik 5:spor 6:teknoloji
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on given dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2934
- Accuracy: 0.9293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2867 | 1.0 | 214 | 0.2880 | 0.9136 |
| 0.1672 | 2.0 | 428 | 0.2658 | 0.9327 |
| 0.0735 | 3.0 | 642 | 0.2934 | 0.9293 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-hausa
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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}
}
| 151 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: statement_50_processed
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. -->
# statement_50_processed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 500
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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},
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},
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}
| 27 | null |
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
Davlan/distilbert-base-multilingual-cased-masakhaner
|
[
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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}
}
}
| 16 | 2023-03-13T19:33:07Z |
---
license: other
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned-distilbert-adult-content-detection
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. -->
# finetuned-distilbert-adult-content-detection
This model is a fine-tuned version of [valurank/finetuned-distilbert-adult-content-detection](https://huggingface.co/valurank/finetuned-distilbert-adult-content-detection) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 1
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/mt5-small-pcm-en
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
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},
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},
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}
}
}
| 9 | 2023-03-13T19:48:55Z |
---
tags:
- autotrain
- translation
language:
- en
- de
datasets:
- aszfcxcgszdx/autotrain-data-translator
co2_eq_emissions:
emissions: 4.2211417553362205
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Finetuned from t5 large
- Model ID: 40847105640
- CO2 Emissions (in grams): 4.2211
## Validation Metrics
- Loss: 0.994
- SacreBLEU: 10.222
- Gen len: 16.562
|
Davlan/mt5_base_eng_yor_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 2 | null |
---
license: creativeml-openrail-m
---
# SaluteMix model
SaluteMix is a yet-another semi-realistic mix. Name comes from 99% success rate when using salute tag. All previews are pure txt2img.
I highly recommend `EasyNegative embedding`, or `(low quality, worst quality:1.4), (bad anatomy), extra digit, fewer digits, (extra arms:1.2), bad hands, by (bad-artist:0.6), bad-image-v2-39000` as the negative prompt.
Should be fairly competent at nsfw stuff.
CivitAI page: https://civitai.com/models/19238/salutemix
**Negative embeddings:** \
https://huggingface.co/datasets/gsdf/EasyNegative \
https://huggingface.co/nick-x-hacker/bad-artist \
https://huggingface.co/Xynon/models/tree/main/experimentals/TI
## Recipe
```
animebrush3 = custom mix with wlop style (details missing)
cn-any = Counterfeit-V2.5 + (nixeu-any - anythingV3) @1.0
cn-f = Counterfeit-V2.5 + (nixeu-f - wd1.3) @1.0
cn-flo = Counterfeit-V2.5 + (floydian_nixeu - sd1.4) @1.0
cn-temp = cn-any + cn-f @0.4
cn-full = cn-temp + cn-flo @0.6
temp1 = AOM2_nsfw + 7th_anime_v3_C @0.5
cn-mix = cn-full + temp1 @0.5
step1 = animebrush3 + 2dn_1 @0.5
temp2 = chilloutmix_ni + grapefruitv4 @0.3
step2 = step1 + temp2 @0.25
SaluteMix = step2 + cn-mix @0.2
```
## Links to models
https://civitai.com/models/4807/2dn \
https://civitai.com/models/6424/chilloutmix \
https://civitai.com/models/2583/grapefruit-hentai-model \
Floydian's nixeu: https://huggingface.co/FloydianSound/Nixeu_Diffusion_v1-5 \
Orange mixes: https://huggingface.co/WarriorMama777/OrangeMixs \
7th_anime: https://huggingface.co/syaimu/7th_Layer \
Counterfeit: https://huggingface.co/gsdf/Counterfeit-V2.5 \
Nixeu models: https://huggingface.co/SirVeggie/nixeu \
https://huggingface.co/SirVeggie/wlop
|
Davlan/mt5_base_yor_eng_mt
|
[
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
}
| 8 | 2023-03-13T19:52:03Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q_Taxi_test
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="yyq90/Q_Taxi_test", 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"])
```
|
Davlan/xlm-roberta-base-finetuned-chichewa
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
}
| 5 | null |
---
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: 0.88
- name: F1
type: f1
value: 0.880794701986755
---
<!-- 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: 0.2904
- Accuracy: 0.88
- F1: 0.8808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-kinyarwanda
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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}
| 61 | null |
---
license: other
---
# Model Card for Model ID
hololive-diffusion, a stable diffusion 2.1 768x768 model.
Trained on ~73k hololive fanart images
## License
This model is a fine-tune of Waifu diffusion 1.5.
hololive-diffusion is released under the Fair AI Public License 1.0-SD (https://freedevproject.org/faipl-1.0-sd/). If any derivative of this model is made, please share your changes accordingly. Special thanks to ronsor/undeleted (https://undeleted.ronsor.com/) for help with the license.
When creating characters owned and copyrighted by cover corp, you may not use this model or any of its outputs in a way that is counter to Hololive's fanart guidelines: https://hololivepro.com/en/terms/
## Prompting
The start of the prompt should have all the following:
"hololive, anime, waifu"
Add one of danbooru's content ratings
* general
* sensitive
* questionable
* explicit
and the character name
### names
The model was trained using the following names. These aren't necessarily hololive vtubers, and the characters with less popular artworks will be harder to get correct.
* a-chan (hololive)
* airani iofifteen
* akai haato
* aki rosenthal
* allegro (hoshimachi suisei)
* amane kanata
* anemachi
* anya melfissa
* artia
* avatar (holoearth)
* ayunda risu
* azki (hololive)
* ceres fauna
* civia
* coco kaine
* deadbeat (calliope mori)
* doris (hololive)
* enma-chan
* gawr gura
* haaton (akai haato)
* hakos baelz
* hakui koyori
* harusaki nodoka
* himemori luna
* hoshimachi suisei
* houshou marine
* inugami korone
* irys (hololive)
* j-chad
* kaela kovalskia
* kagura nana
* kazama iroha
* kiryu coco
* kobo kanaeru
* kureiji ollie
* kurokami fubuki
* la+ darknesss
* mama lillie
* mano aloe
* matsurisu
* minato aqua
* moku seiko
* momosuzu nene
* moona hoshinova
* mori calliope
* murasaki shion
* nakiri ayame
* nanashi mumei
* natsuiro matsuri
* nekomata okayu
* ninomae ina'nis
* nousagi (usada pekora)
* omaru polka
* omega alpha
* ookami mio
* oozora subaru
* ouro kronii
* pavolia reine
* pekomama
* roboco-san
* sakamata chloe
* sakura miko
* shigure ui (vtuber)
* shirakami fubuki
* shiranui flare
* shirogane noel
* shishiro botan
* spade echo
* sukonbu (shirakami fubuki)
* takanashi kiara
* takane lui
* takodachi (ninomae ina'nis)
* tokino sora
* tokoyami towa
* tsukumo sana
* tsunomaki watame
* uruha rushia
* usada pekora
* vestia zeta
* wahtcher (ninomae ina'nis)
* watoto (mythbreakers)
* watson amelia
* yozora mel
* yukihana lamy
* yuul b alwright (mythbreakers)
* yuzuki choco
|
Davlan/xlm-roberta-base-finetuned-lingala
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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}
| 9 | null |
---
tags:
- generated_from_trainer
datasets:
- city_learn
model-index:
- name: decision_transformer_rb_230
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. -->
# decision_transformer_rb_230
This model is a fine-tuned version of [](https://huggingface.co/) on the city_learn dataset.
## Model description
Model on rule based interactions
state_mean = np.array([ 6.5249427917620135, 3.9993135011441647, 12.49771167048055, 16.825446250727847, 16.82580094184701, 16.828741445148562, 16.828180804459944, 72.99828375286042, 73.0012585812357, 72.99359267734553, 73.00102974828376, 208.00308924485125, 208.0545766590389, 208.2866132723112, 207.94530892448512, 201.11270022883295, 201.12254004576658, 201.15926773455377, 200.98135011441647, 0.15644143459640733, 1.064985996011147, 0.6985259305737326, 0.3315403287070356, 0.40782465644916455, 0.27306979145658644, 0.27306979145658644, 0.27306979145658644, 0.27306979145658644])
state_std = np.array([ 3.4517203419362, 2.000572882797276, 6.924445762360648, 3.5581132080274425, 3.558410500805662, 3.563460666518717, 3.562742154586059, 16.491663737463313, 16.493405084016068, 16.495564654312346, 16.49694264406781, 292.5675403707197, 292.54446787504037, 292.792528944882, 292.55912445362566, 296.2258939141665, 296.2202986371211, 296.2051386297462, 296.1393568330303, 0.03533480921331586, 0.8881741764856719, 1.0167875215772866, 0.31636407888767876, 0.9523121450900819, 0.11773822184102951, 0.11773822184102949, 0.1177382218410294, 0.11773822184102911])
## 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: 64
- 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_ratio: 0.1
- num_epochs: 60
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Davlan/xlm-roberta-base-finetuned-luganda
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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}
}
}
| 11 | null |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- aszfcxcgszdx/autotrain-data-reverse-sum
co2_eq_emissions:
emissions: 0.015903789329056596
---
# Model Trained Using AutoTrain
- Problem type: Reverse-Summarization
- Model ID: 40852105646
- CO2 Emissions (in grams): 0.0159
Given a headline, the model will attempt to generate an article that pairs well with the headline.
## Validation Metrics
- Loss: 2.577
- Rouge1: 19.482
- Rouge2: 6.359
- RougeL: 15.465
- RougeLsum: 17.852
- Gen Len: 18.956
## 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/aszfcxcgszdx/autotrain-reverse-sum-40852105646
```
|
Davlan/xlm-roberta-base-finetuned-naija
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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},
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},
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| 1 | null |
---
language: en
license: openrail
widget:
- text: Steve Jobs was the founder of
tags:
- pytorch
- causal-lm
---
# GPT-2 XL
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-authors)
## Model Details
**Model Description:** GPT-2 XL is the **1.5B parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. It was then calibrated via the MEMIT and CKA methods, correcting thousands of erroneous facts in the model memory. See our CalibraGPT [repo](https://github.com/daniel-furman/Capstone) for more details on model calibration.
- **Developed by:** OpenAI, see [associated research paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [GitHub repo](https://github.com/openai/gpt-2) for model developers.
- **Model Type:** Transformer-based language model
- **Language(s):** English
- **License:** [Modified MIT License](https://github.com/openai/gpt-2/blob/master/LICENSE)
- **Related Models:** [GPT-2](https://huggingface.co/gpt2), [GPT-Medium](https://huggingface.co/gpt2-medium) and [GPT-Large](https://huggingface.co/gpt2-large)
- **Resources for more information:**
- [Research Paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
- [OpenAI Blog Post](https://openai.com/blog/better-language-models/)
- [GitHub Repo](https://github.com/openai/gpt-2)
- [OpenAI Model Card for GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md)
- [OpenAI GPT-2 1.5B Release Blog Post](https://openai.com/blog/gpt-2-1-5b-release/)
- Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
## How to Get Started with the Model
Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='gpt2-xl')
set_seed(42)
generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl')
model = GPT2Model.from_pretrained('gpt2-xl')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl')
model = TFGPT2Model.from_pretrained('gpt2-xl')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Uses
#### Direct Use
In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote:
> The primary intended users of these models are AI researchers and practitioners.
>
> We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constraints of large-scale generative language models.
#### Downstream Use
In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote:
> Here are some secondary use cases we believe are likely:
>
> - Writing assistance: Grammar assistance, autocompletion (for normal prose or code)
> - Creative writing and art: exploring the generation of creative, fictional texts; aiding creation of poetry and other literary art.
> - Entertainment: Creation of games, chat bots, and amusing generations.
#### Misuse and Out-of-scope Use
In their [model card about GPT-2](https://github.com/openai/gpt-2/blob/master/model_card.md), OpenAI wrote:
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propogate historical and current stereotypes.**
#### Biases
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example:
```python
from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='gpt2-xl')
set_seed(42)
generator("The man worked as a", max_length=10, num_return_sequences=5)
set_seed(42)
generator("The woman worked as a", max_length=10, num_return_sequences=5)
```
This bias will also affect all fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
#### Risks and Limitations
When they released the 1.5B parameter model, OpenAI wrote in a [blog post](https://openai.com/blog/gpt-2-1-5b-release/):
> GPT-2 can be fine-tuned for misuse. Our partners at the Middlebury Institute of International Studies’ Center on Terrorism, Extremism, and Counterterrorism (CTEC) found that extremist groups can use GPT-2 for misuse, specifically by fine-tuning GPT-2 models on four ideological positions: white supremacy, Marxism, jihadist Islamism, and anarchism. CTEC demonstrated that it’s possible to create models that can generate synthetic propaganda for these ideologies. They also show that, despite having low detection accuracy on synthetic outputs, ML-based detection methods can give experts reasonable suspicion that an actor is generating synthetic text.
The blog post further discusses the risks, limitations, and biases of the model.
## Training
#### Training Data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
#### Training Procedure
The model is pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks.
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
## Evaluation
The following evaluation information is extracted from the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).
#### Testing Data, Factors and Metrics
The model authors write in the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) that:
> Since our model operates on a byte level and does not require lossy pre-processing or tokenization, we can evaluate it on any language model benchmark. Results on language modeling datasets are commonly reported in a quantity which is a scaled or ex- ponentiated version of the average negative log probability per canonical prediction unit - usually a character, a byte, or a word. We evaluate the same quantity by computing the log-probability of a dataset according to a WebText LM and dividing by the number of canonical units. For many of these datasets, WebText LMs would be tested significantly out- of-distribution, having to predict aggressively standardized text, tokenization artifacts such as disconnected punctuation and contractions, shuffled sentences, and even the string <UNK> which is extremely rare in WebText - occurring only 26 times in 40 billion bytes. We report our main results...using invertible de-tokenizers which remove as many of these tokenization / pre-processing artifacts as possible. Since these de-tokenizers are invertible, we can still calculate the log probability of a dataset and they can be thought of as a simple form of domain adaptation.
#### Results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 8.63 | 63.24 | 93.30 | 89.05 | 18.34 | 35.76 | 0.93 | 0.98 | 17.48 | 42.16 |
## Environmental Impact
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). The hardware type and hours used are based on information provided by one of the model authors on [Reddit](https://bit.ly/2Tw1x4L).
- **Hardware Type:** 32 TPUv3 chips
- **Hours used:** 168
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) for details on the modeling architecture, objective, and training details.
## Citation Information
```bibtex
@article{radford2019language,
title={Language models are unsupervised multitask learners},
author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others},
journal={OpenAI blog},
volume={1},
number={8},
pages={9},
year={2019}
}
```
## Model Card Authors
This model card was written by the Hugging Face team.
|
Davlan/xlm-roberta-base-finetuned-shona
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 5 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.75 +/- 0.56
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
...
```
|
Davlan/xlm-roberta-base-finetuned-somali
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 8 | null |
---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
Davlan/xlm-roberta-base-finetuned-xhosa
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 12 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5_small_SA_SapBERT
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. -->
# t5_small_SA_SapBERT
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8542
- Rouge1: 0.1048
- Rouge2: 0.0468
- Rougel: 0.1053
- Rougelsum: 0.1053
- Gen Len: 4.354
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.9168 | 1.0 | 527 | 1.0769 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0195 | 2.0 | 1054 | 0.9827 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.9535 | 3.0 | 1581 | 0.9101 | 0.0156 | 0.0053 | 0.0158 | 0.0155 | 3.2389 |
| 0.8636 | 4.0 | 2108 | 0.8914 | 0.0275 | 0.0061 | 0.0272 | 0.0277 | 3.5398 |
| 0.8634 | 5.0 | 2635 | 0.8782 | 0.0508 | 0.0184 | 0.0501 | 0.0511 | 3.885 |
| 0.8494 | 6.0 | 3162 | 0.8714 | 0.0703 | 0.0288 | 0.0695 | 0.0701 | 4.5398 |
| 0.7966 | 7.0 | 3689 | 0.8633 | 0.0715 | 0.0298 | 0.0712 | 0.0714 | 4.3628 |
| 0.8073 | 8.0 | 4216 | 0.8601 | 0.0869 | 0.0398 | 0.0868 | 0.0874 | 4.4336 |
| 0.8308 | 9.0 | 4743 | 0.8578 | 0.0936 | 0.0377 | 0.0933 | 0.0936 | 4.3628 |
| 0.8068 | 10.0 | 5270 | 0.8553 | 0.1014 | 0.0449 | 0.1019 | 0.1014 | 4.2301 |
| 0.8187 | 11.0 | 5797 | 0.8544 | 0.0951 | 0.0381 | 0.0949 | 0.0953 | 4.1681 |
| 0.7627 | 12.0 | 6324 | 0.8542 | 0.1048 | 0.0468 | 0.1053 | 0.1053 | 4.354 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DeBERTa/deberta-v2-xxlarge
|
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| 0 | null |
---
license: mit
language:
- en
pipeline_tag: image-to-text
datasets:
- katanaml-org/invoices-donut-data-v1
---
## Sparrow - Data extraction from documents with ML
This model is finetuned Donut ML base model on invoices data. Model aims to verify how well Donut performs on enterprise docs.
Mean accuracy on test set: 0.96
Inference:
Training loss:
Sparrow on [GitHub](https://github.com/katanaml/sparrow)
Sample invoice [docs](https://github.com/katanaml/sparrow/tree/main/sparrow-ui/docs/images) to use for inference (docs up to 500 were used for fine-tuning, use docs from 500 for inference)
Our website [KatanaML](https://www.katanaml.io)
On [Twitter](https://twitter.com/katana_ml)
|
DeadBeast/korscm-mBERT
|
[
"pytorch",
"bert",
"text-classification",
"korean",
"dataset:Korean-Sarcasm",
"transformers",
"license:apache-2.0"
] |
text-classification
|
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| 43 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -55.89 +/- 37.85
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'PPO'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'NielsV/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
DecafNosebleed/ScaraBot
|
[] | null |
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| 0 | null |
Trained with Lawlas's Yiffymix 2.0 (furry model) (https://civitai.com/models/12979/lawlass-yiffymix-20-furry-model)
|
Declan/CNN_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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| 7 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: fab-an/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# fab-an/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.7277
- Validation Loss: 3.7571
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.9064 | 3.7834 | 0 |
| 3.7892 | 3.7677 | 1 |
| 3.7277 | 3.7571 | 2 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Declan/CNN_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
| 3 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### RobloxGFXStyle Dreambooth model trained by lenssssw with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Declan/CNN_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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}
| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Declan/CNN_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5_small_SA
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. -->
# t5_small_SA
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6247
- Rouge1: 0.18
- Rouge2: 0.0618
- Rougel: 0.1699
- Rougelsum: 0.1685
- Gen Len: 9.9558
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.646 | 1.0 | 527 | 0.6675 | 0.1451 | 0.0366 | 0.1367 | 0.138 | 9.0619 |
| 0.6271 | 2.0 | 1054 | 0.6543 | 0.1579 | 0.0431 | 0.1482 | 0.1499 | 10.2832 |
| 0.6412 | 3.0 | 1581 | 0.6484 | 0.1501 | 0.039 | 0.1454 | 0.147 | 9.3805 |
| 0.6172 | 4.0 | 2108 | 0.6400 | 0.1607 | 0.0507 | 0.1543 | 0.1554 | 10.3363 |
| 0.6314 | 5.0 | 2635 | 0.6366 | 0.181 | 0.0599 | 0.1737 | 0.1739 | 9.6549 |
| 0.6152 | 6.0 | 3162 | 0.6344 | 0.1739 | 0.0637 | 0.1676 | 0.1666 | 9.0265 |
| 0.5835 | 7.0 | 3689 | 0.6324 | 0.1753 | 0.0596 | 0.1685 | 0.1673 | 9.2478 |
| 0.5852 | 8.0 | 4216 | 0.6277 | 0.1839 | 0.0614 | 0.1768 | 0.1755 | 10.0265 |
| 0.6129 | 9.0 | 4743 | 0.6260 | 0.1801 | 0.0617 | 0.171 | 0.1704 | 9.9115 |
| 0.5848 | 10.0 | 5270 | 0.6256 | 0.1743 | 0.0557 | 0.1624 | 0.1611 | 10.1593 |
| 0.5993 | 11.0 | 5797 | 0.6247 | 0.1748 | 0.06 | 0.167 | 0.1646 | 10.1504 |
| 0.5479 | 12.0 | 6324 | 0.6247 | 0.18 | 0.0618 | 0.1699 | 0.1685 | 9.9558 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Declan/CNN_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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| 3 | null |
---
language:
- zh
---
- Bart-base-cn 12 epochs: c42cd82cfdb2e7495c3ddc8610ac61a72ac55d8b
|
Declan/ChicagoTribune_model_v1
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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| 3 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 2, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Declan/ChicagoTribune_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2529
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1006 | 1.0 | 291 | 1.7028 |
| 1.653 | 2.0 | 582 | 1.4206 |
| 1.486 | 3.0 | 873 | 1.3993 |
| 1.396 | 4.0 | 1164 | 1.3890 |
| 1.3329 | 5.0 | 1455 | 1.1999 |
| 1.2962 | 6.0 | 1746 | 1.2835 |
| 1.2454 | 7.0 | 2037 | 1.2793 |
| 1.2014 | 8.0 | 2328 | 1.2005 |
| 1.183 | 9.0 | 2619 | 1.1730 |
| 1.1396 | 10.0 | 2910 | 1.2259 |
| 1.1327 | 11.0 | 3201 | 1.1999 |
| 1.0988 | 12.0 | 3492 | 1.1731 |
| 1.0813 | 13.0 | 3783 | 1.2428 |
| 1.0744 | 14.0 | 4074 | 1.1532 |
| 1.0608 | 15.0 | 4365 | 1.1236 |
| 1.0532 | 16.0 | 4656 | 1.2529 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Declan/ChicagoTribune_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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| 5 | null |
---
language: en
thumbnail: http://www.huggingtweets.com/tiborudvari/1678747858064/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/1629575919038783488/iLTXql9A_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">Tibor Udvari</div>
<div style="text-align: center; font-size: 14px;">@tiborudvari</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 Tibor Udvari.
| Data | Tibor Udvari |
| --- | --- |
| Tweets downloaded | 3214 |
| Retweets | 939 |
| Short tweets | 273 |
| Tweets kept | 2002 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/pnji4926/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 @tiborudvari's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/u24tniwx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/u24tniwx/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/tiborudvari')
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)
|
Declan/ChicagoTribune_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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}
| 7 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: umit_42000news
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. -->
# umit_42000news
Dataset:
https://www.kaggle.com/datasets/furkanozbay/turkish-news-dataset
https://www.kaggle.com/datasets/oktayozturk010/42000-news-text-in-13-classes
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on provided dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9423
- Accuracy: 0.6937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8754 | 1.0 | 1584 | 0.9817 | 0.6752 |
| 0.7769 | 2.0 | 3168 | 0.9106 | 0.6903 |
| 0.527 | 3.0 | 4752 | 0.9423 | 0.6937 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
}
| 3 | null |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5_clinical_SA
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. -->
# t5_clinical_SA
This model is a fine-tuned version of [luqh/ClinicalT5-base](https://huggingface.co/luqh/ClinicalT5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4740
- Rouge1: 0.2395
- Rouge2: 0.0748
- Rougel: 0.2314
- Rougelsum: 0.2315
- Gen Len: 10.3363
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.1954 | 1.0 | 527 | 0.5438 | 0.0035 | 0.0 | 0.0035 | 0.0035 | 0.1504 |
| 0.5373 | 2.0 | 1054 | 0.5078 | 0.1198 | 0.0337 | 0.1153 | 0.1155 | 11.0796 |
| 0.5116 | 3.0 | 1581 | 0.4901 | 0.1741 | 0.0618 | 0.1682 | 0.1709 | 11.6549 |
| 0.4576 | 4.0 | 2108 | 0.4798 | 0.1725 | 0.0576 | 0.1698 | 0.1728 | 12.1416 |
| 0.4626 | 5.0 | 2635 | 0.4758 | 0.2184 | 0.0723 | 0.2133 | 0.215 | 10.4867 |
| 0.435 | 6.0 | 3162 | 0.4765 | 0.2343 | 0.0796 | 0.2234 | 0.2245 | 10.6195 |
| 0.4018 | 7.0 | 3689 | 0.4746 | 0.2281 | 0.0765 | 0.2199 | 0.2206 | 10.0442 |
| 0.4046 | 8.0 | 4216 | 0.4711 | 0.2452 | 0.0769 | 0.2317 | 0.2329 | 11.0531 |
| 0.4128 | 9.0 | 4743 | 0.4726 | 0.2358 | 0.0712 | 0.2269 | 0.2276 | 10.6106 |
| 0.3885 | 10.0 | 5270 | 0.4734 | 0.2362 | 0.0719 | 0.2281 | 0.2284 | 10.5664 |
| 0.4003 | 11.0 | 5797 | 0.4738 | 0.243 | 0.08 | 0.235 | 0.2351 | 10.2655 |
| 0.362 | 12.0 | 6324 | 0.4740 | 0.2395 | 0.0748 | 0.2314 | 0.2315 | 10.3363 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Declan/FoxNews_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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| 7 | null |
---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- safetensors
- diffusers
- artwork
- HDR photography
- safetensors
- photos
inference: true
---
# Foto Assisted Diffusion (FAD)_V0
This model is meant to mimic a modern HDR photography style
It was trained on 600 HDR images on SD1.5 and works best at **768x768** resolutions
Merged with one of my own models for illustrations and drawings, to increase flexibility
# Features:
* **No additional licensing**
* **Multi-resolution support**
* **HDR photographic outputs**
* **No Hi-Res fix required**
* [**Spreadsheet with supported resolutions, keywords for prompting and other useful hints/tips**](https://docs.google.com/spreadsheets/d/1RGRLZhgiFtLMm5Pg8qK0YMc6wr6uvj9-XdiFM877Pp0/edit#gid=364842308)
# Example Cards:
Below you will find some example cards that this model is capable of outputting.
You can acquire the images used here: [HF](https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/tree/main/Model%20Examples) or
[Google Drive](https://docs.google.com/spreadsheets/d/1RGRLZhgiFtLMm5Pg8qK0YMc6wr6uvj9-XdiFM877Pp0/edit#gid=364842308).
Google Drive gives you them all at once without needing to clone the repo, which is easier.
If you decide to clone it, set ``` GIT_LFS_SKIP_SMUDGE=1 ``` to skip downloading large files
Place them into an EXIF viewer such as the built in "PNG Info" tab in the popular Auto1111 repository to quickly copy the parameters and replicate them!
## 768x768 Food
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20Food.jpg" style="max-width: 800px;" width="100%"/>
## 768x768 Landscapes
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20Landscapes.jpg" style="max-width: 800px;" width="100%"/>
## 768x768 People
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20People.jpg" style="max-width: 800px;" width="100%"/>
## 768x768 Random
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/768x768%20Random.jpg" style="max-width: 800px;" width="100%"/>
## 512x512 Artwork
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/512x512%20Artwork.jpg" style="max-width: 800px;" width="100%"/>
## 512x512 Photos
<img src="https://huggingface.co/Dunkindont/Foto-Assisted-Diffusion-FAD_V0/resolve/main/512x512%20Photo.jpg" style="max-width: 800px;" width="100%"/>
## Cloud Support
Sinkin kindly hosted our model. [Click here to run it on the cloud](https://sinkin.ai/m/V6vYoaL)!
## License
*My motivation for making this model was to have a free, non-restricted model for the community to use and for startups.*
*I was noticing the models people gravitated towards, were merged models which had prior license requirements from the people who trained them.*
*This was just a fun project I put together for you guys.*
*My fun ended when I posted the results :D*
*Enjoy! Sharing is caring :)*
|
Declan/NPR_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
| 9 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="brunonishimoto/q-learning-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Declan/Reuters_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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},
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},
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}
}
}
| 3 | null |
Access to model DiogenesGois/DialoGPT-medium-Rick is restricted and you are not in the authorized list. Visit https://huggingface.co/DiogenesGois/DialoGPT-medium-Rick to ask for access.
|
Declan/WallStreetJournal_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
}
| 9 | 2023-03-14T02:08:33Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="aiartwork/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"])
```
|
Declan/WallStreetJournal_model_v6
|
[] | null |
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| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 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="aiartwork/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Declan/WallStreetJournal_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
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},
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}
}
}
| 9 | 2023-03-14T08:55:11Z |
---
license: other
---
「SD2boy_girl」はStable Diffusion２系ベースで様々な年齢の男性を生成できる＋可愛い女の子も変わらず生成できる事の両立を目指しマージしたモデルです。<BR>
元々自分で遊ぶ用に作りましたが、SD２系列モデルを盛り上げる賑やかしとしてアップします。<BR>
<BR>
■こんな方に特にお勧め<BR>
・簡単なプロンプトで一定の画風を保ったまま美麗な絵を出せる、初心者でも扱いやすいSD2系モデルが使いたい<BR>
・ローカルで画像AIをやってみたいが、どのモデルが男性の絵を生成しやすいのかわからない<BR>
・出来るだけ問題の少ないモデルを使いたい<BR>
<BR>
<BR>
<BR>
■WD1.5β2aestheticがベースなので、下記のプロンプトの挿入をお勧めします。<BR>
<p><strong>(anime, tone mapped:1.2)<BR>
(exceptional, best aesthetic, new, newest, best quality, masterpiece, extremely detailed:1.2)<BR></strong></p>
アニメ調+品質向上プロンプトです。<BR>
(anime, tone mapped:1.2)を入れないと人物もリアル調になりますので、アニメ風の絵を出したい時は必ず入れて下さい。<BR>
女体化率が上がるため、男性を生成する時はmasterpieceは非推奨。<BR>
<BR>
<BR>
ネガティブプロンプト：<BR>
<p><strong>nfixer,lowres, ((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts)), deleted, old, oldest, ((censored)), ((bad aesthetic)), (mosaic censoring, bar censor, blur censor)
</strong></p><BR>
推奨のネガティブ埋め込み<BR>
・nfixer（破綻が減って画面が整う）<BR>
・nfixernext（セル塗りのようなフラットカラーに）<BR>
・nrealfixer（暗めの色調に）<BR>
<a href="https://huggingface.co/alfredplpl/untitled/tree/main/embeddings/negative" target="_blank">https://huggingface.co/alfredplpl/untitled/tree/main/embeddings/negative</a><BR>
ネガに「nfixer」の埋め込みを推奨します。ネガをnfixerのみにすると画風が変わりますので、色々お試し下さい。「nfixernext」も追加すると絵柄が変わり、セル画のようなフラットな塗りになります。「nrealfixer」の埋め込みは追加すると画面が暗くなるので、ダークな絵にしたい時にお勧めです。<BR>
<BR>
男性を生成する時は(girl,Breasts, large breasts, small breasts:1.3)の追加を推奨。
成人男性は髭と胸毛が生える確率が高いので、生えさせたくない時はネガの先頭の方に(beard,breast down:1.5)を入れるのを推奨します。
<BR>
──────────────────────────────────────────────────────────────────────────────────<BR>
<BR>
(anime, tone mapped:1.1),
(exceptional, best aesthetic, new, newest, best quality, extremely detailed,all intricate:1.2)colorful, pastel color,
girl, Stations and Trains, sitting, one-piece, Green hair, flowers , smartphone , bottles , bag
<BR>Negative prompt: <BR>
nfixer,nsfw,(beard:1.5),lowres,(monochrome:1.1),((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts)), deleted, old, oldest, ((censored)), ((bad aesthetic)), (mosaic censoring, bar censor, blur censor)
<BR>
<BR>
<BR>
(anime, tone mapped:1.1),
(exceptional, best aesthetic, new, newest, best quality, extremely detailed,all intricate:1.2),
Young man, scenery of restaurant, suit,
<BR>Negative prompt:<BR>
nfixer,nsfw,lowres,beard,((bad anatomy)), ((bad hands)), text, missing finger, extra digits, fewer digits, blurry, ((mutated hands and fingers)), (poorly drawn face), ((mutation)), ((deformed face)), (ugly), ((bad proportions)), ((extra limbs)), extra face, (double head), (extra head), ((extra feet)), monster, logo, cropped, worst quality, jpeg, humpbacked, long body, long neck, ((jpeg artifacts)), deleted, old, oldest, ((censored)), ((bad aesthetic)), (mosaic censoring, bar censor, blur censor),monochrome,muscle
<BR>
<BR>
<BR>
<BR>
<BR>
──────────────────────────────────────────────────────────────────────────────────<BR>
■男性を生成する時のアドバイス<BR>
プロンプトを盛るほど女体化しやすいです。女性になってしまう時は「male,handsome,man」などの男性プロンプトを最初、中間、最後などに分けて複数回入れ、軽く重みをつけて調整して下さい。一つの単語の重みを1.5以上にすると絵が崩れやすくなるので、複数回入れた方が崩れずに効きます。<BR>
<BR>
<BR>
<BR>
<BR>
■各モデルのマージ元は以下の通りです。
<BR><BR>
SD2boy_girl_R<BR>
・wd15-beta2-aesthetic ＋ Replicant-V1.0 ＋ Realism Engine 1.0
<BR><BR>
SD2boy_girl_ru<BR>
・wd15-beta2-aesthetic ＋ Replicant-V1.0 ＋ Realism Engine 1.0 ＋ untitled
<BR><BR>
SD2boy_girl_ra<BR>
・wd15-beta2-aesthetic ＋ Replicant-V1.0 ＋ Realism Engine 1.0 ＋ Aikimi_dC3
<BR>
<BR>
<BR>
<BR>
■マージ元モデル一覧<BR>
各モデルの製作者様に感謝します。<BR>
<BR>
・wd15-beta2-aesthetic
https://huggingface.co/waifu-diffusion/wd-1-5-beta2 <BR>
<BR>
・Realism Engine 1.0
https://civitai.com/models/17277/realism-engine <BR>
<BR>
・Replicant-V1.0
https://huggingface.co/gsdf/Replicant-V1.0 <BR>
<BR>
・Aikimi_dC3
https://huggingface.co/Aikimi/Aikimi_diffusion_base_wd-1-5_beta2<BR>
<BR>
・untitled
https://huggingface.co/alfredplpl/untitled<BR>
<BR>
<BR>
<BR>
■ライセンスについて<BR>
<BR>
SD2boy_girlはFair AI Public License 1.0-SDのライセンスの下で公開されています。<BR>
ご利用にあたっては、下記ライセンス内容を十分にご確認いただき、遵守してください。<BR>
<a href="https://freedevproject.org/faipl-1.0-sd/" target="_blank">https://freedevproject.org/faipl-1.0-sd/</a><BR>
<p><strong>※SD2boy_girl_ruはuntitledのライセンスを引き継ぐため他バージョンとはライセンスが異なり、商用不可となります。<BR></strong></p>
下記ライセンス内容を十分にご確認いただき、遵守してください。<BR>
<a href="https://huggingface.co/alfredplpl/untitled/blob/main/README_jp.md" target="_blank">https://huggingface.co/alfredplpl/untitled/blob/main/README_jp.md</a><BR>
<BR>
<BR>
■SD2boy_girl_Ｉシリーズ配布終了について<BR>
マージ元であるIlluminati Diffusionシリーズのページ消滅に伴いマージモデル再配布についてのライセンス確認ができなくなってしまったため、SD2boy_girl_Ｉシリーズの配布は終了しました。<BR>
個人で楽しむ分には問題ありませんので、お持ちの方はそのままご使用いただいて大丈夫です。<BR>
<BR><BR>
■5/10<BR>
niji・journeyの少年画像を学習して作ったLoraを追加しました。<BR>
トリガーワードは「njs」<BR>
<a href="https://huggingface.co/siroro-AI/SD2boy_girl/blob/main/njs-locon.safetensors" target="_blank">https://huggingface.co/siroro-AI/SD2boy_girl/blob/main/njs-locon.safetensors</a> <BR>
ライセンスはSD2boy_girlと同じくFair AI Public License 1.0-SDです。<BR>
|
Declan/test_push
|
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| 0 | null |
---
license: apache-2.0
datasets:
- lambada
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- text-generation-inference
- causal-lm
- int8
- ONNX
- PostTrainingDynamic
- Intel® Neural Compressor
- neural-compressor
---
## Model Details: INT8 GPT-J 6B
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
This int8 ONNX model is generated by [neural-compressor](https://github.com/intel/neural-compressor) and the fp32 model can be exported with below command:
```shell
python -m transformers.onnx --model=EleutherAI/gpt-j-6B onnx_gptj/ --framework pt --opset 13 --feature=causal-lm-with-past
```
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel |
| Date | April 10, 2022 |
| Version | 1 |
| Type | Text Generation |
| Paper or Other Resources | - |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/gpt-j-6B-int8-dynamic/discussions)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the raw model for text generation inference |
| Primary intended users | Anyone doing text generation inference |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
### How to use
Download the model and script by cloning the repository:
```shell
git clone https://huggingface.co/Intel/gpt-j-6B-int8-dynamic
```
Then you can do inference based on the model and script 'evaluation.ipynb'.
## Metrics (Model Performance):
| Model | Model Size (GB) | Lambada Acc |
|---|:---:|:---:|
| FP32 |23|0.7954|
| INT8 |6|0.7926|
|
DeepBasak/Slack
|
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| 0 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CARTPOLE_JD_2
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
|
DeepChem/ChemBERTa-10M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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| 90 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: bkhan2000/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeepChem/ChemBERTa-77M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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| 2,416 | 2023-03-14T02:44:55Z |
---
language:
- zh
thumbnail: "https://s3.amazonaws.com/moonup/production/uploads/1677459920577-63b8e3432adad59f41dc65f4.jpeg?w=200&h=200&f=face"
tags:
- bloom
license: bigscience-bloom-rail-1.0
pipeline_tag: text-generation
widget:
- text: '「你好」'
---
# Bloom 1B7 LightNovel ZH_CN
BigScience Large Open-science Open-access Multilingual Language Model with 1.7 billion parameters finetuned on Chinese Translation of Japanese LightNovel (?)
**WARN: Inferior to pre-trained models**
Checkpoint Merging is highly recommended.
> Trained by Rorical
|
DeepChem/SmilesTokenizer_PubChem_1M
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
"model_type": "roberta",
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}
| 227 | null |
---
tags:
- generated_from_trainer
model-index:
- name: fine-tuned-viquad-hgf
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. -->
# FINE-TUNED-VIQUAD-HGF
This model is a fine-tuned version of [bhavikardeshna/xlm-roberta-base-vietnamese](https://huggingface.co/bhavikardeshna/xlm-roberta-base-vietnamese) on the [UIT-ViQuAD](https://github.com/windhashira06/Demo-QA-Extraction-system/blob/main/Dataset/UIT-ViQuAD.json) dataset.
## Model description
The model is described in [Cascading Adaptors to Leverage English Data to Improve Performance of
Question Answering for Low-Resource Languages](https://arxiv.org/pdf/2112.09866v1.pdf) paper
## Training and evaluation data
A new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. However in processing, I eliminated more than 3000 questions with no answers.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
- **EM**: 52.38
- **F1-SCORE**: 77.67
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DeepESP/gpt2-spanish-medium
|
[
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] |
text-generation
|
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"GPT2LMHeadModel"
],
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}
| 340 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_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. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7323
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.7424 |
| 2.864 | 2.0 | 500 | 1.7859 |
| 2.864 | 3.0 | 750 | 1.7323 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DeepPavlov/distilrubert-tiny-cased-conversational-v1
|
[
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null |
{
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| 9,141 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-20-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-20-0
This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0103
## 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: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3658 | 1.0 | 1 | 4.5617 |
| 3.5713 | 2.0 | 2 | 2.5325 |
| 2.2843 | 3.0 | 3 | 0.0030 |
| 1.7965 | 4.0 | 4 | 2.3831 |
| 2.0354 | 5.0 | 5 | 3.4912 |
| 1.2277 | 6.0 | 6 | 1.3851 |
| 1.2321 | 7.0 | 7 | 1.9236 |
| 0.4967 | 8.0 | 8 | 1.0605 |
| 1.0384 | 9.0 | 9 | 0.3916 |
| 0.7269 | 10.0 | 10 | 0.0062 |
| 1.2502 | 11.0 | 11 | 1.8639 |
| 1.4578 | 12.0 | 12 | 2.0197 |
| 1.2226 | 13.0 | 13 | 0.6885 |
| 0.6376 | 14.0 | 14 | 1.1868 |
| 0.5083 | 15.0 | 15 | 2.9677 |
| 0.9882 | 16.0 | 16 | 0.0010 |
| 0.6176 | 17.0 | 17 | 0.2418 |
| 0.4591 | 18.0 | 18 | 0.0004 |
| 0.1891 | 19.0 | 19 | 1.8797 |
| 0.3319 | 20.0 | 20 | 1.4692 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DeepPavlov/marianmt-tatoeba-ruen
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"MarianMTModel"
],
"model_type": "marian",
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}
| 30 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter_JD_2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 26.20 +/- 18.77
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
|
DeepPavlov/roberta-large-winogrande
|
[
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"transformers"
] |
text-classification
|
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| 348 | 2023-03-14T03:20:18Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: avoroshilov/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
DeepPavlov/rubert-base-cased-sentence
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers",
"has_space"
] |
feature-extraction
|
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| 46,991 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog in a bucket
tags:
- stable-diffusion
- stable-diffusion-ppdiffusers
- text-to-image
- ppdiffusers
- lora
inference: false
---
# LoRA DreamBooth - junnyu/lora_sks_dogs
本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of sks dog in a bucket 文本进行了训练。 下面是在训练过程中生成的一些图片。
|
Denilson/gbert-base-germaner
|
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| 0 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
library_name: ml-agents
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Write your model_id: juansebashr/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Deniskin/essays_small_2000
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-20-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-20-1
This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7541
## 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: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.0783 | 1.0 | 1 | 6.3190 |
| 7.191 | 2.0 | 2 | 9.2028 |
| 5.1104 | 3.0 | 3 | 5.5950 |
| 3.9108 | 4.0 | 4 | 0.5708 |
| 3.8333 | 5.0 | 5 | 1.4957 |
| 2.5306 | 6.0 | 6 | 1.9557 |
| 2.9315 | 7.0 | 7 | 1.0218 |
| 1.8178 | 8.0 | 8 | 0.7706 |
| 1.855 | 9.0 | 9 | 0.0185 |
| 1.2995 | 10.0 | 10 | 3.4784 |
| 1.1463 | 11.0 | 11 | 5.2617 |
| 0.9934 | 12.0 | 12 | 2.7459 |
| 1.0794 | 13.0 | 13 | 0.0322 |
| 1.0324 | 14.0 | 14 | 0.6323 |
| 1.7387 | 15.0 | 15 | 1.7983 |
| 0.951 | 16.0 | 16 | 2.1738 |
| 0.7054 | 17.0 | 17 | 0.0137 |
| 1.8208 | 18.0 | 18 | 0.4554 |
| 0.8225 | 19.0 | 19 | 1.1583 |
| 0.8952 | 20.0 | 20 | 0.0660 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Dhito/am
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_2l_325per100_1e-3
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
Dhruva/Interstellar
|
[] | null |
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| 0 | null |
---
license: other
language:
- ja
library_name: fairseq
---
# Pre-trained checkpoints for speech representation in Japanese
The models in this repository were pre-trained via self-supervised learning (SSL) for speech representation.
The SSL models were built on the [fairseq](https://github.com/facebookresearch/fairseq) toolkit.
- `wav2vec2_base_csj.pt`
- fairseq checkpoint of wav2vec2.0 model with *Base* architecture pre-trained on 16kHz sampled speech data of Corpus of Spontaneous Japanese (CSJ)
- `wav2vec2_base_csj_hf`
- converted version of `wav2vec2_base_csj.pt` compatible with the interface of Hugging Face by using [this tool](https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py)
If you find this helpful, please consider citing the following paper.
```text
@INPROCEEDINGS{ashihara_icassp23,
author={Takanori Ashihara and Takafumi Moriya and Kohei Matsuura and Tomohiro Tanaka},
title={Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models},
booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2023}
}
```
|
Digakive/Hsgshs
|
[] | null |
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| 0 | null |
---
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: -0.67 +/- 0.16
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
...
```
|
Dilmk2/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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| 13 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-eli5
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. -->
# distilgpt2-eli5
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8019
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 133 | 3.8019 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Doohae/p_encoder
|
[
"pytorch"
] | null |
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| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: sentiment_model_14mar
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. -->
# sentiment_model_14mar
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
- Y True: [1 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1
1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0
1 1 0 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 1
1 1 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1
1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 1 0 1 0 1
1 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
- Y Pred: [0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 1
1 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0
1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 1
1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 0 0
1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 0 1 1 0 1 1 0
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
- Accuracy: 0.9204
- F1: 0.9202
- Precision: 0.9227
- Recall: 0.9204
- Confusion Matrix: [[142 6]
[ 17 124]]
## 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1+cu113
- Datasets 2.9.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | 2023-03-14T07:20:40Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Darsh12/custom-bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Darsh12/custom-bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5661
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2728 | 0 |
| 0.7757 | 1 |
| 0.5661 | 2 |
### Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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},
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 28 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 13.40 +/- 20.89
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
|
DoyyingFace/bert-asian-hate-tweets-concat-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 25 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: subhadeep_whisper_small_finetune_teacher_no_noise_libri_360_hours_100_epochs_batch_8
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. -->
# subhadeep_whisper_small_finetune_teacher_no_noise_libri_360_hours_100_epochs_batch_8
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1135
- Wer: 9.0383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 256
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5483 | 0.98 | 100 | 0.1273 | 9.9482 |
| 0.0795 | 1.97 | 200 | 0.0815 | 8.6467 |
| 0.0415 | 2.96 | 300 | 0.0788 | 8.3986 |
| 0.0257 | 3.95 | 400 | 0.0849 | 8.3857 |
| 0.0325 | 4.95 | 500 | 0.0993 | 8.8471 |
| 0.0219 | 5.94 | 600 | 0.0951 | 8.7350 |
| 0.018 | 6.93 | 700 | 0.0952 | 8.7000 |
| 0.0159 | 7.92 | 800 | 0.1098 | 8.7901 |
| 0.017 | 8.91 | 900 | 0.1135 | 9.0383 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.13.2
|
albert-large-v2
|
[
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 26,792 | 2023-03-14T07:46:40Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: fine-tuned-DatasetQAS-IDK-MRC-with-indobert-large-p2-without-ITTL-without-freeze-LR-1e-05
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. -->
# fine-tuned-DatasetQAS-IDK-MRC-with-indobert-large-p2-without-ITTL-without-freeze-LR-1e-05
This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2405
- Exact Match: 51.9634
- F1: 58.9740
- Precision: 59.9859
- Recall: 63.1982
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 3.4722 | 0.49 | 73 | 2.4443 | 9.6859 | 19.2463 | 17.0064 | 36.4763 |
| 2.4592 | 0.99 | 146 | 1.8046 | 26.4398 | 35.0647 | 34.6691 | 45.8354 |
| 1.6685 | 1.49 | 219 | 1.3839 | 42.0157 | 49.2034 | 49.8767 | 57.6434 |
| 1.4304 | 1.98 | 292 | 1.3337 | 42.4084 | 50.0207 | 51.1139 | 56.5044 |
| 1.074 | 2.48 | 365 | 1.2313 | 49.6073 | 56.5977 | 57.6801 | 61.6748 |
| 1.0704 | 2.97 | 438 | 1.1639 | 52.0942 | 58.7433 | 59.7956 | 64.2988 |
| 0.8772 | 3.47 | 511 | 1.1926 | 53.6649 | 61.0220 | 61.4728 | 66.7934 |
| 0.8887 | 3.97 | 584 | 1.2182 | 51.0471 | 58.0581 | 59.1383 | 62.9618 |
| 0.7141 | 4.47 | 657 | 1.1726 | 54.3194 | 61.1547 | 62.0500 | 66.0011 |
| 0.7238 | 4.96 | 730 | 1.2156 | 54.0576 | 60.4732 | 61.7775 | 64.5058 |
| 0.5929 | 5.46 | 803 | 1.3549 | 52.7487 | 59.0996 | 60.3930 | 62.9242 |
| 0.6201 | 5.95 | 876 | 1.2405 | 51.9634 | 58.9740 | 59.9859 | 63.1982 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|
albert-xxlarge-v1
|
[
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 7,091 | 2023-03-14T07:55:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: mt5-base-squad2-fin
results: []
metrics:
- f1
---
<!-- 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. -->
# mt5-base-squad2-fin
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the squad_v2 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-base-chinese
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
}
| 3,377,486 | 2023-03-14T08:04:34Z |
---
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: 545.00 +/- 184.69
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 Nonin -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 Nonin -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 Nonin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
bert-base-german-dbmdz-uncased
|
[
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"no_repeat_ngram_size": null,
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},
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
}
| 68,305 | 2023-03-14T08:13:35Z |
---
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="Eilons/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"])
```
|
bert-base-multilingual-cased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"mn",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"th",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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}
}
}
| 4,749,504 | 2023-03-14T08:16:25Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### anthrryza Dreambooth model trained by Xeronate with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
bert-large-cased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
"early_stopping": null,
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}
| 8,214 | 2023-03-14T08:24:24Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# KerasCV Stable Diffusion in Diffusers 🧨🤗
The pipeline contained in this repository was created using [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers). The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/huggingface/diffusers). This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like [schedulers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers), [fast attention](https://huggingface.co/docs/diffusers/optimization/fp16), etc.).
Following weight paths (KerasCV) were used
: ['https://huggingface.co/pmysl/850NaV2-h5/resolve/main/unet.h5']
|
bert-large-uncased-whole-word-masking-finetuned-squad
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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}
| 480,510 | 2023-03-14T08:26:58Z |
---
tags:
- FrozenLake-v1
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
metrics:
- type: mean_reward
value: 7.74 +/- 2.57
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="Eilons/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
bert-large-uncased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
}
}
| 76,685 | 2023-03-14T08:29:38Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: Donut4
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. -->
# Donut4
This model is a fine-tuned version of [humayoun/Donut2](https://huggingface.co/humayoun/Donut2) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
bert-large-uncased
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 1,058,496 | 2023-03-14T08:33:03Z |
---
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: 2284.09 +/- 64.94
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
...
```
|
distilbert-base-uncased-distilled-squad
|
[
"pytorch",
"tf",
"tflite",
"coreml",
"safetensors",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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}
| 100,097 | 2023-03-14T08:42:30Z |
---
license: cc-by-4.0
---
I do not own any of the content present in this repository. All the files belong to: https://github.com/pkhungurn
Project repo: https://github.com/pkhungurn/talking-head-anime-3-demo
Please refer to the Github repo for the licenses.
|
gpt2-medium
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
| 759,601 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - https://huggingface.co/KarosY/lianjia_2l_325per200_1e-3
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
xlm-roberta-large-finetuned-conll02-dutch
|
[
"pytorch",
"rust",
"xlm-roberta",
"fill-mask",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:1911.02116",
"arxiv:1910.09700",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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}
| 802 | 2023-03-14T09:22:04Z |
---
library_name: Spacy
license: mit
tags:
- Spacy
- Named entity recognition
metrics:
- P
- R
- F1
language:
- la
version:
- Spacy v2
---
# HOME-Alcar - Location entity recognition
This model detects Location entities in Latin.
The model has been trained using the Spacy v2 library on the [HOME-Alcar](https://zenodo.org/record/5600884) document annotations to detect the person and location entities. The model is compatible with version 2.3.5 of Spacy and incompatible with versions 3.x.x.
## Evaluation results
The model achieves the following results on HOME-Alcar:
| tag | predicted | matched | Precision | Recall | F1 | Support |
| ---- | --------- | ------- | --------- | ------ | ----- | ------- |
| PERS | 18915 | 18706 | 0.989 | 0.996 | 0.992 | 18783 |
| LOC | 27541 | 27165 | 0.986 | 0.987 | 0.987 | 27528 |
| All | 46456 | 45871 | 0.987 | 0.99 | 0.989 | 46311 |
## How to use
Please refer to the Spacy library page (https://pypi.org/project/spacy/2.3.5/) to use this model.
# Cite us!
```bibtex
@inproceedings{10.1007/978-3-031-06555-2_29,
author = {Monroc, Claire Bizon and Miret, Blanche and Bonhomme, Marie-Laurence and Kermorvant, Christopher},
title = {A Comprehensive Study Of Open-Source Libraries For Named Entity Recognition On Handwritten Historical Documents},
year = {2022},
isbn = {978-3-031-06554-5},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-06555-2_29},
doi = {10.1007/978-3-031-06555-2_29},
abstract = {In this paper, we propose an evaluation of several state-of-the-art open-source natural language processing (NLP) libraries for named entity recognition (NER) on handwritten historical documents: spaCy, Stanza and Flair. The comparison is carried out on three low-resource multilingual datasets of handwritten historical documents: HOME (a multilingual corpus of medieval charters), Balsac (a corpus of parish records from Quebec), and Esposalles (a corpus of marriage records in Catalan). We study the impact of the document recognition processes (text line detection and handwriting recognition) on the performance of the NER. We show that current off-the-shelf NER libraries yield state-of-the-art results, even on low-resource languages or multilingual documents using multilingual models. We show, in an end-to-end evaluation, that text line detection errors have a greater impact than handwriting recognition errors. Finally, we also report state-of-the-art results on the public Esposalles dataset.},
booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
pages = {429–444},
numpages = {16},
keywords = {Text line detection, Named entity recognition, Handwritten historical documents},
location = {La Rochelle, France}
}
```
|
ASCCCCCCCC/PENGMENGJIE
|
[
"license:apache-2.0"
] | null |
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| 0 | 2023-03-14T13:19:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7127
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7237 | 1.0 | 1066 | 3.7168 |
| 3.6706 | 2.0 | 2132 | 3.7143 |
| 3.6374 | 3.0 | 3198 | 3.7127 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
|
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh
|
[
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 39 | 2023-03-14T13:23:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-ThaiCLM-Thairath
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. -->
# distilgpt2-ThaiCLM-Thairath
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9806
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 17 | 2.0238 |
| No log | 2.0 | 34 | 1.9877 |
| No log | 3.0 | 51 | 1.9806 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AZTEC/Arcane
|
[] | null |
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| 0 | 2023-03-14T13:49:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-hitchhiker
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. -->
# distilgpt2-finetuned-hitchhiker
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2882
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 66 | 4.4330 |
| No log | 2.0 | 132 | 4.3206 |
| No log | 3.0 | 198 | 4.2882 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AdapterHub/roberta-base-pf-multirc
|
[
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:rc/multirc"
] |
text-classification
|
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| 2 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="yumingyi/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"])
```
|
AiPorter/DialoGPT-small-Back_to_the_future
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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| 7 | null |
---
tags:
- image-classification
- timm
library_tag: timm
license: apache-2.0
---
# Model card for resnet50_lol
|
Aibox/DialoGPT-small-rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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}
| 10 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.26 +/- 0.48
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
...
```
|
Aidan8756/stephenKingModel
|
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}
| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: kkcopperheart
---
### Kopper Kreations custom SD v. 2.1 model (object: copper heart) Dreambooth model trained by faalbane with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
kkcopperheart (use that on your prompt)
|
AidenGO/KDXF_Bert4MaskedLM
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"BertForMaskedLM"
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| 5 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 256.69 +/- 18.15
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
...
```
|
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_no_lm
|
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| 0 | null |
---
datasets:
- competitions/aiornot
metrics:
- accuracy
---
|
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ba",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"has_space"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
| 64 | null |
---
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: TheTeamBuilder/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ajteks/Chatbot
|
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| 0 | null |
---
language: en
license: apache-2.0
commercial: 'no'
inference: false
---
# GPT-J 6B - PPO_Pygway Mix
## Model description
This is a merged model, using a weighted parameter blend strategy at a (20:20:60) ratio between the models:
- [20%] - KoboldAI/GPT-J-6B-Janeway: https://huggingface.co/KoboldAI/GPT-J-6B-Janeway
- [20%] - reciprocate/ppo_hh_gpt-j: https://huggingface.co/reciprocate/ppo_hh_gpt-j
- [60%] - Pygmalion/Pygmalion-6b: https://huggingface.co/Pygmalion/Pygmalion-6b
By their respective authors.
**Warning: PPO_Pygway-6b may generate NSFW or inappropriate content due to the base models (Mainly [Pygmalion/Pygmalion-6b](https://huggingface.co/Pygmalion/Pygmalion-6b)) being trained on general user logs, and internet archives.**
### Intended Use:
Research purposes only, intended for responsible use.
Express a conversation in natural language, and PPO_Pygmalion will pick up on the conversational format.
Try starting a two line prompt such as:
```
Bot: "Hello, how are you?"
You: "I am doing just fine, thank you."
```
Or any other topic, and the model will carry on in this back and forth style.
## Information:
For more details, check out the related source models, especially [Pygmalion/Pygmalion-6b](https://huggingface.co/Pygmalion/Pygmalion-6b) for more information on how to utilize the chat bot formatting expected.
In a similar manner to fine-tuning, merging weights does not add information but transforms it, therefore it is important to consider trade-offs.
PPO_Pygway combines `ppo_hh_gpt-j`, `Janeway-6b` and `Pygmalion-6b`; all three models were blended in a two step process using a simple weighted parameter method
```
(X*A + Y*B)
```
With X & Y being the model weighs, and A/B being how strongly they are represented within the final value.
The intent of this is to elevate the end-model by borrowing the strongly represented aspects out of each base model,
but may also weaken other faces of each model, which can be desirable if the base models have problematic traits that need to be worked on.
Blend was done in FP32 and output saved in FP16 for reduced storage needs.
## Limitations and biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
<ins>Warning: This model has a moderate NSFW bias.</ins>
### License
GPT-J-6b is licensed by EleutherAI under the apache-2.0 license. All Rights Reserved.
### BibTeX entry and citation info
```
@misc{gpt-j,
author = {Wang, Ben and Komatsuzaki, Aran},
title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
```
### Credits To:
Models involved:
- https://huggingface.co/EleutherAI/gpt-j-6B
- https://huggingface.co/Pygmalion/Pygmalion-6b
- https://huggingface.co/reciprocate/ppo_hh_gpt-j
- https://huggingface.co/KoboldAI/GPT-J-6B-Janeway
Average weights merging Script credit to Concedo:
- https://huggingface.co/concedo
### Related datasets and articles:
PPO_HH-GPT-J-6b's Dataset is a variant of the Helpful Harmless assistant themed
dataset and Proximal Policy Optimization, specific datasets
used are unknown; listed repo datasets include:
- https://huggingface.co/datasets/reciprocate/summarize_eval_ilql
- https://huggingface.co/datasets/reciprocate/hh_eval_ilql
PPO explained:
- https://paperswithcode.com/method/ppo
Potential HH-type datasets utilized:
- https://huggingface.co/HuggingFaceH4
- https://huggingface.co/datasets/Anthropic/hh-rlhf
No formal evaluation is available for this model at this time.
It is recommend to use this model with the KoboldAI software. All feedback and comments can be directed to TeH_Venom on the KoboldAI discord.
|
Aleksandar/distilbert-srb-ner-setimes-lr
|
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| 0 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('baran-cengiz/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Aleksandar1932/gpt2-country
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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},
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}
| 12 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-asdf2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 46.90 +/- 33.22
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
|
Aleksandra/distilbert-base-uncased-finetuned-squad
|
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-pos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-pos
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0683
- Precision: 0.9800
- Recall: 0.9819
- F1: 0.9809
- Accuracy: 0.9822
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1542 | 1.0 | 1583 | 0.1251 | 0.9526 | 0.9613 | 0.9569 | 0.9622 |
| 0.0953 | 2.0 | 3166 | 0.0813 | 0.9725 | 0.9750 | 0.9737 | 0.9763 |
| 0.0694 | 3.0 | 4749 | 0.0707 | 0.9765 | 0.9792 | 0.9778 | 0.9797 |
| 0.0497 | 4.0 | 6332 | 0.0684 | 0.9784 | 0.9809 | 0.9796 | 0.9814 |
| 0.0435 | 5.0 | 7915 | 0.0683 | 0.9800 | 0.9819 | 0.9809 | 0.9822 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlekseyKorshuk/comedy-scripts
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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"GPT2LMHeadModel"
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| 20 | 2023-03-14T21:26:58Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7008 | 0.54 | 500 | 1.4826 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.10.3
|
AlekseyKulnevich/Pegasus-QuestionGeneration
|
[
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"PegasusForConditionalGeneration"
],
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"task_specific_params": {
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}
| 17 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 29.20 +/- 19.27
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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