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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-14 06:27:53
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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listlengths 1
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| pipeline_tag
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acidhills/sd-class-butterflies-32 | acidhills | 2022-12-18T14:55:17Z | 6 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2022-12-18T14:54:51Z | ---
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('acidhills/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
ziemke/q-Taxi-v3 | ziemke | 2022-12-18T14:46:16Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T14:15:02Z | ---
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: 7.56 +/- 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="ziemke/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"])
```
|
lambdaofgod/document_nbow_embedder | lambdaofgod | 2022-12-18T14:45:13Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-18T14:45:06Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# lambdaofgod/document_nbow_embedder
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 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('lambdaofgod/document_nbow_embedder')
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=lambdaofgod/document_nbow_embedder)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(84046, 200)
)
(1): WordWeights(
(emb_layer): Embedding(84046, 1)
)
(2): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
lambdaofgod/query_nbow_embedder | lambdaofgod | 2022-12-18T14:44:55Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-18T14:44:50Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# lambdaofgod/query_nbow_embedder
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 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('lambdaofgod/query_nbow_embedder')
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=lambdaofgod/query_nbow_embedder)
## Full Model Architecture
```
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(6912, 200)
)
(1): WordWeights(
(emb_layer): Embedding(6912, 1)
)
(2): Pooling({'word_embedding_dimension': 200, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
RedPandaAINLP/Taxi-v3-lr05-ms199-ep1M | RedPandaAINLP | 2022-12-18T14:00:38Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T14:00:31Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-lr05-ms199-ep1M
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="RedPandaAINLP/Taxi-v3-lr05-ms199-ep1M", 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"])
```
|
Finnish-NLP/whisper-large-v2-finnish | Finnish-NLP | 2022-12-18T13:57:57Z | 17 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"finnish",
"fi",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-17T11:11:25Z | ---
language:
- fi
license: apache-2.0
tags:
- whisper-event
- finnish
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
- cer
model-index:
- name: Whisper Large V2 Finnish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: fi
split: test
args: fi
metrics:
- name: Wer
type: wer
value: 10.42
- name: Cer
type: cer
value: 1.91
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: FLEURS
type: google/fleurs
config: fi_fi
split: test
args: fi_fi
metrics:
- name: Wer
type: wer
value: 10.2
- name: Cer
type: cer
value: 3.36
--- |
Payoto/t5-small-finetuned-xsum | Payoto | 2022-12-18T13:51:34Z | 84 | 0 | transformers | [
"transformers",
"pytorch",
"optimum_graphcore",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-11-14T18:25:48Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
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-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5273
## 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
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- training precision: Mixed Precision
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6962 | 1.0 | 3188 | 2.5273 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1
|
MaxReimann/WISE-APDrawing-XDoG | MaxReimann | 2022-12-18T13:49:32Z | 0 | 0 | null | [
"arxiv:2207.14606",
"license:mit",
"region:us"
]
| null | 2022-12-18T13:00:40Z | ---
license: mit
---
This model can create line-drawings using an xDoG algorithmic effect with predicted parametrizations.
[Demo space of framework](https://huggingface.co/spaces/MaxReimann/Whitebox-Style-Transfer-Editing)
<br>
Code for framework: [https://github.com/winfried-ripken/wise](https://github.com/winfried-ripken/wise)
<br>
Paper on Arxiv: [arxiv/2207.14606](https://arxiv.org/abs/2207.14606)
<img src='https://huggingface.co/MaxReimann/WISE-APDrawing-XDoG/resolve/main/xdog_apdrawing.jpg'/>
[WISE: Whitebox Image Stylization by Example-based Learning](https://ivpg.hpi3d.de/wise)
[Winfried Lötzsch](https://scholar.google.de/citations?user=wAVKdLcAAAAJ&hl=de)\*<sup>1</sup>,
[Max Reimann](https://hpi.de/doellner/people/max-reimann.html)\*<sup>1</sup>,
[Martin Büßemeyer](https://www.researchgate.net/profile/Martin-Buessemeyer)<sup>1</sup>,
[Amir Semmo](http://asemmo.github.io/)<sup>2</sup>,
[Jürgen Döllner](https://hpi.de/forschung/fachgebiete/computergrafische-systeme.html)<sup>1</sup>,
[Matthias Trapp](https://hpi.de/doellner/people/trapp.html)<sup>1</sup> <br>
<sup>1</sup>Hasso Plattner Institute, University of Potsdam, Germany, <sup>2</sup>Digitalmasterpieces GmbH, Germany<br/>
\*denotes equal contribution
in ECCV 2022
``` latex
@InProceedings{loetzsch2022wise,
title={WISE: Whitebox Image Stylization by Example-based Learning},
author={Lötzsch, Winfried and Reimann, Max and Büssemeyer, Martin and Semmo, Amir and Döllner, Jürgen and Trapp, Matthias},
title="WISE: Whitebox Image Stylization by Example-Based Learning",
booktitle="Computer Vision -- ECCV 2022",
year="2022"
}
``` |
RedPandaAINLP/Taxi-v3-lr05-ms199 | RedPandaAINLP | 2022-12-18T13:43:46Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T13:41:05Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-lr05-ms199
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="RedPandaAINLP/Taxi-v3-lr05-ms199", 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"])
```
|
Honza/Taxi-v3 | Honza | 2022-12-18T13:41:40Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T13:41:32Z | ---
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="Honza/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"])
```
|
RedPandaAINLP/q-FrozenLake-v1-4x4-noSlippery | RedPandaAINLP | 2022-12-18T13:34:40Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T13:34:30Z | ---
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="RedPandaAINLP/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"])
```
|
Payoto/roberta-base-finetuned-squad | Payoto | 2022-12-18T13:28:43Z | 67 | 0 | transformers | [
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-11-17T18:40:43Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-finetuned-squad
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. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1
|
ahmadmwali/finetuning-sentiment-hausa21 | ahmadmwali | 2022-12-18T13:24:52Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-18T10:58:31Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-hausa21
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-hausa21
This model is a fine-tuned version of [mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1444
- Accuracy: 0.9586
- F1: 0.9586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Honza/q-FrozenLake-v1-4x4-noSlippery | Honza | 2022-12-18T13:16:44Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T13:16:40Z | ---
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="Honza/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"])
```
|
Payoto/roberta-base-finetuned-swag | Payoto | 2022-12-18T13:12:48Z | 36 | 0 | transformers | [
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"generated_from_trainer",
"dataset:swag",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2022-11-14T12:16:42Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: roberta-base-finetuned-swag
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. -->
# roberta-base-finetuned-swag
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4382
- Accuracy: 0.8390
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- training precision: Mixed Precision
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5707 | 1.0 | 574 | 0.4990 | 0.8097 |
| 0.5092 | 2.0 | 1148 | 0.4321 | 0.8361 |
| 0.3597 | 3.0 | 1722 | 0.4382 | 0.8390 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1
|
leviethoang/wav2vec2-large-xls-r-300m-vi-75p | leviethoang | 2022-12-18T13:09:07Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T09:25:35Z | ---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-vi-75p
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-vi-75p
This model is a fine-tuned version of [leviethoang/wav2vec2-large-xls-r-300m-vi-25p](https://huggingface.co/leviethoang/wav2vec2-large-xls-r-300m-vi-25p) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7880
- Wer: 0.4324
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.962 | 1.68 | 400 | 1.2033 | 0.4428 |
| 0.7977 | 3.36 | 800 | 1.3410 | 0.4731 |
| 0.644 | 5.04 | 1200 | 1.4682 | 0.4796 |
| 0.5156 | 6.72 | 1600 | 1.4940 | 0.4826 |
| 0.4531 | 8.4 | 2000 | 1.5071 | 0.4734 |
| 0.3882 | 10.08 | 2400 | 1.5408 | 0.4694 |
| 0.3469 | 11.76 | 2800 | 1.5975 | 0.4697 |
| 0.3096 | 13.45 | 3200 | 1.7120 | 0.4728 |
| 0.2825 | 15.13 | 3600 | 1.7052 | 0.4632 |
| 0.2607 | 16.81 | 4000 | 1.6870 | 0.4575 |
| 0.2301 | 18.49 | 4400 | 1.7205 | 0.4653 |
| 0.2096 | 20.17 | 4800 | 1.7352 | 0.4504 |
| 0.1915 | 21.85 | 5200 | 1.7948 | 0.4465 |
| 0.1685 | 23.53 | 5600 | 1.7994 | 0.4400 |
| 0.1543 | 25.21 | 6000 | 1.7613 | 0.4435 |
| 0.1378 | 26.89 | 6400 | 1.8300 | 0.4365 |
| 0.1278 | 28.57 | 6800 | 1.7880 | 0.4324 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
MrDivakaruni/ppo-LunarLander-v2 | MrDivakaruni | 2022-12-18T12:49:26Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T10:44:56Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 237.63 +/- 20.23
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
...
```
|
Payoto/roberta-base-finetuned-cola | Payoto | 2022-12-18T12:41:07Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"optimum_graphcore",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-11-14T12:00:49Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: roberta-base-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-cola
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5127
- Matthews Correlation: 0.5815
## 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
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- training precision: Mixed Precision
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.3138 | 1.0 | 133 | 0.4104 | 0.5538 |
| 0.2082 | 2.0 | 266 | 0.4849 | 0.5402 |
| 0.1687 | 3.0 | 399 | 0.5127 | 0.5815 |
| 0.0865 | 4.0 | 532 | 0.5752 | 0.5661 |
| 0.1008 | 5.0 | 665 | 0.5952 | 0.5764 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cpu
- Datasets 2.7.1
- Tokenizers 0.12.1
|
ales/whisper-base-belarusian | ales | 2022-12-18T12:35:28Z | 20 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"be",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-16T21:30:37Z | ---
language:
- be
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Base Belarusian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 be
type: mozilla-foundation/common_voice_11_0
config: be
split: validation
args: be
metrics:
- name: Wer
type: wer
value: 12.206885082321635
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Base Belarusian
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 be dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1080
- Wer: 12.2069
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2445 | 0.17 | 1000 | 0.3059 | 32.4163 |
| 0.1823 | 0.33 | 2000 | 0.2004 | 22.1259 |
| 0.1412 | 0.5 | 3000 | 0.1752 | 20.0700 |
| 0.1093 | 0.67 | 4000 | 0.1413 | 16.0533 |
| 0.1137 | 0.83 | 5000 | 0.1155 | 13.3108 |
| 0.0585 | 1.1 | 6000 | 0.1080 | 12.2069 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
tashatsar/ppo-LunarLander-v2-updates | tashatsar | 2022-12-18T12:31:14Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T12:30:45Z | ---
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: 296.52 +/- 12.94
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
...
```
|
kohya-ss/kawase-hasui-diffusion | kohya-ss | 2022-12-18T12:29:59Z | 0 | 14 | null | [
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2022-12-18T11:02:11Z | ---
license: creativeml-openrail-m
language:
- en
tags:
- stable-diffusion
- text-to-image
---
Kawase Hasui Diffusion is trained on pantings by [KAWASE Hasui（川瀬巴水）](https://en.wikipedia.org/wiki/Hasui_Kawase).
The model has been trained on Stable Diffusion v2-1 with DreamBooth method with a learning rate of 1.0e-6 for 2,600 steps with the batch size of 8 (8 train or reg images) on 169 training images and 664 regularization images.
This model is based on SD2.1 768/v, so if you use this model in the poplular Web UI, please rename 'v2-inference-v.yaml' to 'kawase-hasui-epoch-000003.yaml' (or ~_fp16.yaml) and place it to the same folder to .safetensors.
The training prompt is "picture by lvl".
## Examples
```
picture by lvl, japan tourism poster
seed : 968191097, sampler: k_euler_a, steps : 160, CFG scale : 5.5
```
```
picture by lvl, cyberpunk akihabara
seed : 1418478714, sampler: k_euler_a, steps : 160, CFG scale : 5.5
```
```
picture by lvl, ruined castle, fantasy, dawn
seed : 897433524, sampler: k_euler_a, steps : 160, CFG scale : 5.5
```
```
picture by lvl, fantasy, party of adventurers, ready to fight, in front of ruined temple
seed : 1814292911, sampler: k_euler_a, steps : 160, CFG scale : 5.5
```
## License
CreativeML Open RAIL-M
|
tagotec/ppo-LunarLander-v2 | tagotec | 2022-12-18T12:20:00Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T12:19:36Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.91 +/- 16.15
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
...
```
|
arampacha/whisper-large-hy | arampacha | 2022-12-18T12:15:04Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"hy",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-14T11:27:53Z | ---
language:
- hy
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: whisper-base-hy
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hy-AM
split: test
args: hy-AM
metrics:
- name: Wer
type: wer
value: 22.36842105263158
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-hy
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2204
- Wer: 22.3684
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1394 | 5.87 | 400 | 0.1780 | 28.2895 |
| 0.0536 | 11.75 | 800 | 0.1739 | 24.6053 |
| 0.0247 | 17.64 | 1200 | 0.2098 | 22.9605 |
| 0.0154 | 23.52 | 1600 | 0.2035 | 22.1382 |
| 0.0103 | 29.41 | 2000 | 0.2204 | 22.3684 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Daehoon/PPO-LunarLander-v2 | Daehoon | 2022-12-18T12:05:10Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T12:04:37Z | ---
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: 254.77 +/- 28.24
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
...
```
|
jlondonobo/whisper-large-v2-es | jlondonobo | 2022-12-18T11:32:26Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"es",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T04:30:24Z | ---
language:
- es
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large V2 Spanish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 es
type: mozilla-foundation/common_voice_11_0
config: es
split: test
args: es
metrics:
- name: Wer
type: wer
value: 5.074450392391248
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V2 Spanish
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 es dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1648
- Wer: 5.0745
## 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-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1556 | 0.5 | 750 | 0.1683 | 5.0959 |
| 0.1732 | 1.35 | 1500 | 0.1648 | 5.0745 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
ruzarx/Taxi | ruzarx | 2022-12-18T11:09:34Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T09:29:50Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ruzarx/Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
anuragshas/whisper-large-v2-ml | anuragshas | 2022-12-18T11:02:10Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"ml",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-12T20:46:25Z | ---
language:
- ml
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large-v2 Malayalam
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 ml
type: mozilla-foundation/common_voice_11_0
config: ml
split: test
args: ml
metrics:
- name: Wer
type: wer
value: 25.478927203065133
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large-v2 Malayalam
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 ml dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4170
- Wer: 25.4789
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0 | 71.01 | 1000 | 0.4170 | 25.4789 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
heegyu/kobart-text-style-transfer | heegyu | 2022-12-18T10:39:49Z | 95 | 6 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-18T09:59:31Z | kobart 모델에 Korean Smilestyle Dataset을 파인튜닝한 한국어 텍스트 스타일 변환 모델
예제
```
styles = ['문어체','구어체','안드로이드','아재','채팅',
'초등학생','이모티콘','enfp','신사','할아버지','할머니','중학생',
'왕','나루토','선비','소심한','번역기']
model = pipeline(
'text2text-generation',
model='heegyu/kobart-text-style-transfer'
)
def transfer_text_style(text, target_style, **kwargs):
input = f"{target_style} 말투로 변환:{text}"
out = model(input, max_length=64, **kwargs)
print(text, target_style, out[0]['generated_text'], sep="->")
text = "반가운. 나는 6마리의 고양이를 소지하고 있다."
for style in styles:
transfer_text_style(text, style)
```
결과
```
반가운. 나는 6마리의 고양이를 소지하고 있다.->문어체->안녕하세요. 저는 6마리의 고양이를 가지고 있습니다.
반가운. 나는 6마리의 고양이를 소지하고 있다.->구어체->안녕. 나는 6마리의 고양이를 가지고 있어.
반가운. 나는 6마리의 고양이를 소지하고 있다.->안드로이드->반갑다. 안드로이드. 6마리. 고양이. 보유.
반가운. 나는 6마리의 고양이를 소지하고 있다.->아재->안녕~~~~ 6마리의 고양이를 가지고 있네
반가운. 나는 6마리의 고양이를 소지하고 있다.->채팅->하이~ 6마리의 고양이 있음
반가운. 나는 6마리의 고양이를 소지하고 있다.->초등학생->ᄒᄋ 난 6마리 고양이 ᄏᄏ
반가운. 나는 6마리의 고양이를 소지하고 있다.->이모티콘->안녕!~()~ 난 6마리의 고양이를 가지고 있어 (皿)
반가운. 나는 6마리의 고양이를 소지하고 있다.->enfp->안녕!!~ 난 6마리의 고양이를 둬!
반가운. 나는 6마리의 고양이를 소지하고 있다.->신사->안녕하십니까, 저는 6마리의 고양이를 가지고 있습니다.
반가운. 나는 6마리의 고양이를 소지하고 있다.->할아버지->안녕하신가...나는 6마리의 고양이를 가지고 있구먼...
반가운. 나는 6마리의 고양이를 소지하고 있다.->할머니->염병 염병할 고양이 놈이여
반가운. 나는 6마리의 고양이를 소지하고 있다.->중학생->ᄒᄋ 난 6마리 고양이 키우는데
반가운. 나는 6마리의 고양이를 소지하고 있다.->왕->반갑소. 짐은 6마리의 고양이를 소유하고 있소.
반가운. 나는 6마리의 고양이를 소지하고 있다.->나루토->안녕하냐니깐! 난 6마리의 고양이를 가지고 있다니깐!
반가운. 나는 6마리의 고양이를 소지하고 있다.->선비->안녕하시오! 소생은 6마리의 고양이를 가지고 있소!
반가운. 나는 6마리의 고양이를 소지하고 있다.->소심한->안녕.... 난 6마리 고양이 있어..
반가운. 나는 6마리의 고양이를 소지하고 있다.->번역기->반가운, 나는 6마리의 고양이를 가지고 있다.
``` |
emmyapi/distilbart-podimo-data-5 | emmyapi | 2022-12-18T10:25:14Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"Summarization",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-11-15T16:26:15Z | ---
tasks: summarization
license: apache-2.0
tags:
- generated_from_trainer
- Summarization
model-index:
- name: distilbart-podimo-data-5
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. -->
# distilbart-podimo-data-5
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1325
## Model description
model | rouge1 | rouge2 | rougeL | rougeLsum
--- | --- | --- | --- |---
sshleifer/distilbart-cnn-12-6 | 0.202654 | 0.025766 | 0.123072 | 0.130183
emmyapi/distilbart-podimo-data-3 | 0.235147 | 0.047087 | 0.151535 | 0.161782
emmyapi/distilbart-podimo-data-4 | 0.236926 | 0.048327 | 0.153539 | 0.165026
emmyapi/distilbart-podimo-data-5 | 0.259024 | 0.061665 | 0.167187 | 0.178399
emmyapi/distilbart-podimo-data-7 | 0.298888 | 0.059900 | 0.159479 | 0.185049
## 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: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3477 | 3.33 | 500 | 3.7027 |
| 2.6286 | 6.66 | 1000 | 3.6995 |
| 2.0718 | 10.0 | 1500 | 3.8868 |
| 1.7806 | 13.33 | 2000 | 4.1325 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
vaibhav9/distilbert-base-uncased-finetuned-squad | vaibhav9 | 2022-12-18T10:05:13Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-12-17T11:18:33Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5239
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4335 | 1.0 | 3748 | 1.4521 |
| 1.0869 | 2.0 | 7496 | 1.4054 |
| 0.8612 | 3.0 | 11244 | 1.5239 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
philschmid/tiny-distilbert-classification | philschmid | 2022-12-18T10:04:35Z | 55 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-03-02T23:29:05Z | # Test model
> ## This model is used to run tests for the Hugging Face DLCs |
ginton/ppo-LunarLander-v2 | ginton | 2022-12-18T10:02:13Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T20:52:40Z | ---
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: 239.00 +/- 67.42
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ruzarx/q-FrozenLake-v1-4x4-noSlippery | ruzarx | 2022-12-18T09:26:37Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T09:26:19Z | ---
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="ruzarx/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"])
```
|
dhandapanip/Ss | dhandapanip | 2022-12-18T08:09:02Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
]
| null | 2022-12-18T08:09:02Z | ---
license: bigscience-bloom-rail-1.0
---
|
nu-dialogue/sfc2022-stable-diffusion | nu-dialogue | 2022-12-18T07:20:46Z | 16 | 3 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"ja",
"japanese",
"arxiv:2112.10752",
"license:other",
"diffusers:JapaneseStableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-18T04:50:44Z | ---
language: ja
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- ja
- japanese
inference: true
# extra_gated_prompt: |-
# One more step before getting this model.
# This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
# The CreativeML OpenRAIL License specifies:
# 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
# 2. rinna Co., Ltd. claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
# 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
# Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
# By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
# extra_gated_fields:
# I have read the License and agree with its terms: checkbox
---
# SFCOCO Stable Diffusion Model Card
SFCOCO Stable Diffusion is a Japanese-specific latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
This model was fine-tuned by using a powerful Japanese-specific latent text-to-image diffusion model, [Japanese Stable Diffusion](https://huggingface.co/rinna/japanese-stable-diffusion).
We use the [Stable Diffusion text-to-image fine-tuning script](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) of [🤗 Diffusers](https://github.com/huggingface/diffusers)
[](https://colab.research.google.com/github/nu-dialogue/clip-prefix-caption-jp/blob/master/notebooks/sfc2022_stable_diffusion.ipynb)
## Model Details
- **Developed by:** Atsumoto Ohashi
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** Japanese
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model (LDM)](https://arxiv.org/abs/2112.10752) that used [Japanese Stable Diffusion](https://huggingface.co/rinna/japanese-stable-diffusion) as a pre-trained model.
- **Resources for more information:** [Japanese Stable Diffusion GitHub Repository](https://github.com/rinnakk/japanese-stable-diffusion)
## Examples
Firstly, install our package as follows. This package is modified [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Japanese Stable Diffusion.
```bash
pip install git+https://github.com/rinnakk/japanese-stable-diffusion
```
Run this command to log in with your HF Hub token if you haven't before:
```bash
huggingface-cli login
```
Running the pipeline with the k_lms scheduler:
```python
import torch
from torch import autocast
from diffusers import LMSDiscreteScheduler
from japanese_stable_diffusion import JapaneseStableDiffusionPipeline
model_id = "nu-dialogue/sfc2022-stable-diffusion"
device = "cuda"
# Use the K-LMS scheduler here instead
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
pipe = JapaneseStableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, use_auth_token=True, torch_dtype=torch.float16)
pipe = pipe.to(device)
prompt = "福澤諭吉像の写真"
with autocast("cuda"):
image = pipe(prompt, guidance_scale=7.5)["sample"][0]
image.save("output.png")
```
_Note: `JapaneseStableDiffusionPipeline` is almost same as diffusers' `StableDiffusionPipeline` but added some lines to initialize our models properly._
## Training
**Training Data**
We used the SFCOCO2021 and SFCOCO2022 dataset for training the model.
You can see these datasets in [this repository](https://github.com/nu-dialogue/clip-prefix-caption-jp).
**Training Procedure**
SFCOCO Stable Diffusion has the same architecture as Japanese Stable Diffusion and was trained by using Japanese Stable Diffusion.
We use the [Stable Diffusion text-to-image fine-tuning script](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) of [🤗 Diffusers](https://github.com/huggingface/diffusers)
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
```bibtex
@misc{japanese_stable_diffusion,
author = {Shing, Makoto and Sawada, Kei},
title = {Japanese Stable Diffusion},
howpublished = {\url{https://github.com/rinnakk/japanese-stable-diffusion}},
month = {September},
year = {2022},
}
```
*This model card was written by: Atsumoto Ohashi and is based on the [Japanese Stable Diffusion Model Card](https://github.com/rinnakk/japanese-stable-diffusion).* |
vjkrish/lunarLander | vjkrish | 2022-12-18T04:24:06Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T04:11:38Z | ---
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: -606.02 +/- 190.89
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
...
```
|
Scrya/whisper-medium-id | Scrya | 2022-12-18T04:09:37Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-16T16:41:14Z | ---
language:
- id
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
model-index:
- name: Whisper Medium ID - FLEURS-CV
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: id_id
split: test
metrics:
- type: wer
value: 7.8
name: WER
- type: cer
value: 2.43
name: CER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: id
split: test
metrics:
- type: wer
value: 8.67
name: WER
- type: cer
value: 2.71
name: CER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium ID - FLEURS-CV
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2563
- eval_wer: 8.4690
- eval_runtime: 2961.9108
- eval_samples_per_second: 1.453
- eval_steps_per_second: 0.091
- epoch: 14.29
- step: 5000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
wooihen/ppo-LunarLander-v2-TEST2 | wooihen | 2022-12-18T03:43:24Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-15T17:53:51Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.95 +/- 11.94
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
...
```
|
sumedh/biomedical_text_summarization | sumedh | 2022-12-18T03:11:14Z | 14 | 2 | transformers | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"summarization",
"en",
"dataset:sumedh/MeQSum",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2022-12-18T00:23:04Z | ---
tags:
- summarization
language:
- en
widget:
- text: Type your medical text here. 🤗
datasets:
- sumedh/MeQSum
co2_eq_emissions:
emissions: 3198.3976606503647
model-index:
- name: sumedh/biomedical_text_summarization
results:
- task:
type: summarization
name: Summarization
metrics:
- name: ROUGE-1
type: rouge
value: 39.4086
verified: true
- name: ROUGE-2
type: rouge
value: 12.8115
verified: true
- name: ROUGE-L
type: rouge
value: 21.9191
verified: true
- name: ROUGE-LSUM
type: rouge
value: 35.2431
verified: true
- name: loss
type: loss
value: 2.2001051902770996
verified: true
- name: gen_len
type: gen_len
value: 133.8541
verified: true
---
This model was created for text summarization for clinical text.
Check the index for evaluation scores on the ROUGE metric. |
mustfkeskin/ppo-LunarLander-v2 | mustfkeskin | 2022-12-18T02:46:25Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T02:45:59Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.56 +/- 22.06
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
...
```
|
nlp-cimat/politibeto | nlp-cimat | 2022-12-18T02:45:19Z | 10 | 4 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"masked-lm",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-06-20T18:09:39Z | ---
language:
- es
tags:
- masked-lm
widget:
- text: "La mayor ventaja de la democracia es su [MASK]."
example_title: "Ejemplo 1"
---
# PolitiBETO: A Spanish BERT adapted to a language domain of Political Tweets
PolitiBETO is a [BERT model](https://github.com/google-research/bert) tailored for political tasks in social media corpora. It is a Domain Adaptation on top of [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased), a pretrained BERT in Spanish.
This model is meant to be fine-tuned for downstream tasks.
## Citation
[NLP-CIMAT at PoliticEs 2022: PolitiBETO, a Domain-Adapted Transformer for Multi-class Political Author Profiling](https://ceur-ws.org/Vol-3202/politices-paper2.pdf)
To cite this in a publication please use the following:
```
@inproceedings{PolitiBeto2022,
title={{NLP-CIMAT} at {P}olitic{E}s 2022: {P}oliti{BETO}, a {D}omain-{A}dapted {T}ransformer for {M}ulti-class {P}olitical {A}uthor {P}rofiling},
author={Emilio Villa-Cueva and Ivan Gonz{\'a}lez-Franco and Fernando Sanchez-Vega and Adri{\'a}n Pastor L{\'o}pez-Monroy},
booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022)},
series = {{CEUR} Workshop Proceedings},
publisher = {CEUR-WS},
year={2022}
}
``` |
AinTziLLo/ppo-LunarLander-v2 | AinTziLLo | 2022-12-18T02:27:35Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T01:11:11Z | ---
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: 285.39 +/- 21.24
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
...
```
|
greedypiggy/ppo-Huggy | greedypiggy | 2022-12-18T01:59:16Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-18T01:59:08Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: greedypiggy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
geninhu/whisper-medium-gl | geninhu | 2022-12-18T01:20:31Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"gl",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-17T14:24:54Z | ---
language:
- gl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Galician
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 gl
type: mozilla-foundation/common_voice_11_0
config: gl
split: test
args: gl
metrics:
- name: Wer
type: wer
value: 8.41678391128031
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium Galician
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 gl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2864
- Wer: 8.4168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0074 | 6.01 | 1000 | 0.2564 | 8.8927 |
| 0.0006 | 12.03 | 2000 | 0.2864 | 8.4168 |
| 0.0003 | 19.01 | 3000 | 0.3043 | 8.5078 |
| 0.0002 | 25.02 | 4000 | 0.3145 | 8.4913 |
| 0.0002 | 32.01 | 5000 | 0.3189 | 8.4706 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
jlondonobo/whisper-large-v2-pt-v3 | jlondonobo | 2022-12-18T01:19:32Z | 14 | 5 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"pt",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-17T18:57:25Z | ---
language:
- pt
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large Portuguese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 pt
type: mozilla-foundation/common_voice_11_0
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 4.8385198634858195
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large Portuguese
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 pt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1503
- Wer: 4.8385
## 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-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1526 | 0.33 | 500 | 0.1588 | 4.9074 |
| 0.1046 | 1.3 | 1000 | 0.1510 | 4.8806 |
| 0.079 | 2.28 | 1500 | 0.1503 | 4.8385 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
toastedshibe/ppo-LunarLander-v2 | toastedshibe | 2022-12-18T00:30:25Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-18T00:20:14Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.60 +/- 15.87
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
...
```
|
pittawat/autotrain-twitter-covid-19-spam-detection-2512177276 | pittawat | 2022-12-18T00:20:04Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"autotrain",
"text-classification",
"en",
"dataset:pittawat/autotrain-data-twitter-covid-19-spam-detection",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-18T00:19:06Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- pittawat/autotrain-data-twitter-covid-19-spam-detection
co2_eq_emissions:
emissions: 1.0218403202204225
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2512177276
- CO2 Emissions (in grams): 1.0218
## Validation Metrics
- Loss: 0.275
- Accuracy: 0.906
- Precision: 0.930
- Recall: 0.960
- AUC: 0.882
- F1: 0.945
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/pittawat/autotrain-twitter-covid-19-spam-detection-2512177276
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("pittawat/autotrain-twitter-covid-19-spam-detection-2512177276", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("pittawat/autotrain-twitter-covid-19-spam-detection-2512177276", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
PawanUP85/Arcpro | PawanUP85 | 2022-12-17T23:40:10Z | 0 | 0 | null | [
"license:bsd-3-clause-clear",
"region:us"
]
| null | 2022-12-17T23:39:15Z | ---
license: bsd-3-clause-clear
---
git lfs install
git clone https://huggingface.co/PawanUP85/Arcpro |
Balthamos/chantum-test-q | Balthamos | 2022-12-17T23:36:38Z | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-17T03:35:15Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: chantum1
---
### Chantum Test q Dreambooth model trained by Balthamos with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 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!
chantum1 (use that on your prompt)
|
camenduru/xformers-hf-a10g | camenduru | 2022-12-17T23:29:19Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-05T11:22:57Z | ---
title: xformers-hf-a10g
emoji: 🚀
colorFrom: indigo
colorTo: indigo
pinned: false
---
https://github.com/camenduru/stable-diffusion-webui-colab/releases
|
DrishtiSharma/whisper-small-hindi-3k-steps | DrishtiSharma | 2022-12-17T22:59:19Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-17T20:58:54Z | ---
language:
- hi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Hindi - Drishti Sharma
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 16.67658639318744
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hindi - Drishti Sharma
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3013
- Wer: 16.6766
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0188 | 3.67 | 3000 | 0.3013 | 16.6766 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
spayot/hf-drl-unit1bonus-ppo-Huggy | spayot | 2022-12-17T22:40:30Z | 13 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-17T22:40:18Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: spayot/hf-drl-unit1bonus-ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Cesar514/ppo-Huggy | Cesar514 | 2022-12-17T22:16:06Z | 17 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-17T22:15:46Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Cesar514/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AsmaAsma/my-awesome-setfit-model | AsmaAsma | 2022-12-17T21:31:02Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-15T18:09:31Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"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": 28,
"warmup_steps": 3,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Zionamsalem/ppo-Huggy | Zionamsalem | 2022-12-17T20:54:10Z | 24 | 1 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-17T20:54:03Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: Zionamsalem/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
linksaiyajin/q-Taxi-v3 | linksaiyajin | 2022-12-17T20:46:17Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T20:46:08Z | ---
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: 7.52 +/- 2.62
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="linksaiyajin/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
andge/ppo-Huggy | andge | 2022-12-17T20:38:21Z | 13 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-17T20:38:07Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: andge/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Musha-the-Yusha/PPO-LunarLander-V2 | Musha-the-Yusha | 2022-12-17T20:35:12Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-16T17:10:33Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 271.35 +/- 19.79
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AgentXXX/ppo-LunarLander-v2-TEST | AgentXXX | 2022-12-17T20:18:00Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T20:15:50Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 289.58 +/- 23.12
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
...
```
|
sd-concepts-library/painting-made-by-bruegel-v4 | sd-concepts-library | 2022-12-17T20:02:41Z | 0 | 4 | null | [
"license:mit",
"region:us"
]
| null | 2022-12-17T18:01:28Z | ---
license: mit
---
### painting made by bruegel V4 on Stable Diffusion
This version includes entire paintings, as well as close ups.
This is the `<bruegel-style-artwork>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Using stabilityai/stable-diffusion-2-base
Example output:
Here is the new concept you will be able to use as a `style`:
|
cxyz/mrnlthkrrgl | cxyz | 2022-12-17T19:45:55Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-17T19:32:31Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### mrnlthkrrgl Dreambooth model trained by cxyz 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)
Or you can 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)
Sample pictures of this concept:
|
dkuznetsov/ppo-LunarLander-v2 | dkuznetsov | 2022-12-17T19:40:09Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T15:34:49Z | ---
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: 286.41 +/- 19.02
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
...
```
|
howtodowtle/adiaz-1-not-good | howtodowtle | 2022-12-17T19:12:46Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-17T19:09:25Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### adiaz_1_not_good Dreambooth model trained by howtodowtle 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)
Or you can 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)
Sample pictures of this concept:
|
Sambosis/distilbert-base-uncased-finetuned-squad | Sambosis | 2022-12-17T19:01:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-12-11T18:37:46Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
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: 2.1904
## 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: 0.0002
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6224 | 1.0 | 692 | 1.1812 |
| 1.0216 | 2.0 | 1384 | 1.2495 |
| 0.5638 | 3.0 | 2076 | 1.3098 |
| 0.3679 | 4.0 | 2768 | 1.6784 |
| 0.2703 | 5.0 | 3460 | 1.8842 |
| 0.1057 | 6.0 | 4152 | 2.1904 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
qanastek/whisper-tiny-french-cased | qanastek | 2022-12-17T18:58:39Z | 25 | 3 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"fr",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-17T12:14:38Z | ---
language:
- fr
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Tiny French Cased
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 fr
type: mozilla-foundation/common_voice_11_0
config: fr
split: test
args: fr
metrics:
- name: Wer
type: wer
value: 33.06549172161867
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs fr_fr
type: google/fleurs
config: fr_fr
split: test
args: fr_fr
metrics:
- name: Wer
type: wer
value: 36.69
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: facebook/voxpopuli fr
type: facebook/voxpopuli
config: fr
split: test
args: fr
metrics:
- name: Wer
type: wer
value: 32.71
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny French Cased
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6509
- Wer on `mozilla-foundation/common_voice_11_0` `fr`: 33.0655
- Wer on `google/fleurs` `fr_fr`: 36.69
- Wer on `facebook/voxpopuli` `fr`: 32.71
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7185 | 0.2 | 1000 | 0.7608 | 38.1636 |
| 0.6052 | 1.2 | 2000 | 0.6949 | 34.9513 |
| 0.4467 | 2.2 | 3000 | 0.6708 | 34.3393 |
| 0.4773 | 3.2 | 4000 | 0.6536 | 33.2102 |
| 0.4479 | 4.2 | 5000 | 0.6509 | 33.0655 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
sinsforeal/furuderika | sinsforeal | 2022-12-17T18:49:17Z | 0 | 3 | null | [
"license:openrail",
"region:us"
]
| null | 2022-12-17T18:05:19Z | ---
license: openrail
---
Furude Rika from Higurashi No Naku Koro Ni. This is a dreambooth that was trained on 71 768 resolution images using the Webui dreambooth extension.
I used batch size 4 and 2 gradient accumulation steps which are the optimal dreambooth settings for my system. You can summon Rika with "furude rika" you can add "school unform",
"shrine maiden outfit", "pe uniform" or "sundress" after "furude rika" to see some of her different outfits.
"school uniform"
"shrine maiden outfit"
"sundress"
"pe uniform"
|
polejowska/vit-vit-base-patch16-224-in21k-eurosat | polejowska | 2022-12-17T18:36:59Z | 21 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-12-17T17:32:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-vit-base-patch16-224-in21k-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.988641975308642
---
<!-- 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-vit-base-patch16-224-in21k-eurosat
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0957
- Accuracy: 0.9886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3303 | 0.99 | 147 | 0.2950 | 0.9790 |
| 0.1632 | 1.99 | 294 | 0.1593 | 0.9842 |
| 0.1097 | 2.99 | 441 | 0.1223 | 0.9859 |
| 0.0868 | 3.99 | 588 | 0.1053 | 0.9877 |
| 0.0651 | 4.99 | 735 | 0.0957 | 0.9886 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
dor88/q-FrozenLake-v1-4x4-noSlippery | dor88 | 2022-12-17T18:35:46Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T18:35:42Z | ---
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="dor88/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"], is_slippery=False)
```
|
jackson-lucas/q-Taxi-v3-v2 | jackson-lucas | 2022-12-17T18:18:13Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T18:11:57Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v2
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="jackson-lucas/q-Taxi-v3-v2", 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"])
```
|
Aileenvl/ppo-LunarLander-v2 | Aileenvl | 2022-12-17T18:17:15Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T18:16:48Z | ---
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: 244.26 +/- 14.26
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
...
```
|
sgangireddy/whisper-medium-cv-fleurs-tr-3k | sgangireddy | 2022-12-17T18:02:55Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-15T10:41:59Z | ---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
metrics:
- wer
model-index:
- name: openai/whisper-medium
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. -->
# openai/whisper-medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2406
- Wer: 10.0333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0241 | 1.06 | 1000 | 0.1996 | 10.4543 |
| 0.009 | 2.12 | 2000 | 0.2156 | 10.1152 |
| 0.0045 | 3.19 | 3000 | 0.2406 | 10.0333 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
jackson-lucas/q-Taxi-v3 | jackson-lucas | 2022-12-17T17:55:48Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T17:38:20Z | ---
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: 7.56 +/- 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="jackson-lucas/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"])
```
|
J4F4N4F/Huggy | J4F4N4F | 2022-12-17T17:54:57Z | 14 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-17T17:54:44Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: J4F4N4F/Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sheldon-spock/q-Taxi-v3 | sheldon-spock | 2022-12-17T17:53:44Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T17:53:38Z | ---
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 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="sheldon-spock/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"])
```
|
sheldon-spock/q-FrozenLake-v1-4x4-noSlippery | sheldon-spock | 2022-12-17T17:51:03Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T17:50:05Z | ---
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="sheldon-spock/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"])
```
|
bnriiitb/whisper-small-te-4k | bnriiitb | 2022-12-17T17:49:33Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"te",
"dataset:IndicSUPERB_train_validation_splits",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-16T14:47:05Z | ---
language:
- te
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- IndicSUPERB_train_validation_splits
metrics:
- wer
model-index:
- name: Whisper Small Telugu - Naga Budigam
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IndicSUPERB train and validation splits
type: IndicSUPERB train and validation splits
config: None
split: None
args: 'config: te, split: test'
metrics:
- name: Wer
type: wer
value: 38.14924740301039
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Telugu - Naga Budigam
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Chai_Bisket_Stories_16-08-2021_14-17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2875
- Wer: 38.1492
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 15000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2064 | 0.66 | 500 | 0.2053 | 60.1707 |
| 0.1399 | 1.33 | 1000 | 0.1535 | 49.3269 |
| 0.1093 | 1.99 | 1500 | 0.1365 | 44.5516 |
| 0.0771 | 2.66 | 2000 | 0.1316 | 42.1136 |
| 0.0508 | 3.32 | 2500 | 0.1395 | 41.1384 |
| 0.0498 | 3.99 | 3000 | 0.1386 | 40.5395 |
| 0.0302 | 4.65 | 3500 | 0.1529 | 40.9529 |
| 0.0157 | 5.32 | 4000 | 0.1719 | 40.6667 |
| 0.0183 | 5.98 | 4500 | 0.1723 | 40.3646 |
| 0.0083 | 6.65 | 5000 | 0.1911 | 40.4335 |
| 0.0061 | 7.31 | 5500 | 0.2109 | 40.4176 |
| 0.0055 | 7.98 | 6000 | 0.2075 | 39.7021 |
| 0.0039 | 8.64 | 6500 | 0.2186 | 40.2639 |
| 0.0026 | 9.31 | 7000 | 0.2254 | 39.1032 |
| 0.0035 | 9.97 | 7500 | 0.2289 | 39.2834 |
| 0.0016 | 10.64 | 8000 | 0.2332 | 39.1456 |
| 0.0016 | 11.3 | 8500 | 0.2395 | 39.4371 |
| 0.0016 | 11.97 | 9000 | 0.2447 | 39.2410 |
| 0.0009 | 12.63 | 9500 | 0.2548 | 38.7799 |
| 0.0008 | 13.3 | 10000 | 0.2551 | 38.7481 |
| 0.0008 | 13.96 | 10500 | 0.2621 | 38.8276 |
| 0.0007 | 14.63 | 11000 | 0.2633 | 38.6686 |
| 0.0003 | 15.29 | 11500 | 0.2711 | 38.4566 |
| 0.0005 | 15.96 | 12000 | 0.2772 | 38.7852 |
| 0.0001 | 16.62 | 12500 | 0.2771 | 38.2658 |
| 0.0001 | 17.29 | 13000 | 0.2808 | 38.2393 |
| 0.0001 | 17.95 | 13500 | 0.2815 | 38.1810 |
| 0.0 | 18.62 | 14000 | 0.2854 | 38.2022 |
| 0.0 | 19.28 | 14500 | 0.2872 | 38.1333 |
| 0.0 | 19.95 | 15000 | 0.2875 | 38.1492 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
thiagoabreulima/dqn-SpaceInvadersNoFrameskip-v4 | thiagoabreulima | 2022-12-17T17:43:35Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T16:16:12Z | ---
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: 6.50 +/- 16.29
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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga thiagoabreulima -f logs/
python enjoy.py --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 thiagoabreulima -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 thiagoabreulima
```
## 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', 0.001),
('learning_starts', 100000),
('n_timesteps', 300000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Schoolar/ppo-LunarLander-v2 | Schoolar | 2022-12-17T17:38:39Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T17:38:00Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PP0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -225.45 +/- 28.06
name: mean_reward
verified: false
---
# **PP0** Agent playing **LunarLander-v2**
This is a trained model of a **PP0** 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
...
```
|
Murdokai/novasessaodidicowe | Murdokai | 2022-12-17T17:33:10Z | 3 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-17T17:27:29Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### novasessaodidicowe Dreambooth model trained by Murdokai 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)
Or you can 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)
Sample pictures of this concept:
|
polejowska/vit-convnext-tiny-224-eurosat | polejowska | 2022-12-17T17:23:59Z | 21 | 0 | transformers | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-12-17T16:34:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-convnext-tiny-224-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9859259259259259
---
<!-- 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-convnext-tiny-224-eurosat
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0576
- Accuracy: 0.9859
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2881 | 0.99 | 147 | 0.2325 | 0.9588 |
| 0.0869 | 1.99 | 294 | 0.0912 | 0.9753 |
| 0.0687 | 2.99 | 441 | 0.0663 | 0.9805 |
| 0.0272 | 3.99 | 588 | 0.0576 | 0.9859 |
| 0.0247 | 4.99 | 735 | 0.0532 | 0.9854 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
RajMoodley/q-Taxi-v3 | RajMoodley | 2022-12-17T17:22:13Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T17:22:08Z | ---
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: 7.56 +/- 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="RajMoodley/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"])
```
|
kuntalcse006/finetuning-sentiment-model-3000-samples | kuntalcse006 | 2022-12-17T17:17:00Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-17T16:57:22Z | ---
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: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.875
---
<!-- 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: 0.3093
- Accuracy: 0.8733
- F1: 0.875
## 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 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
nepp1d0/prot_bert_classification_finetuned_training_script_trial | nepp1d0 | 2022-12-17T16:52:10Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-17T16:50:22Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: prot_bert_classification_finetuned_training_script_trial
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. -->
# prot_bert_classification_finetuned_training_script_trial
This model is a fine-tuned version of [nepp1d0/prot_bert-finetuned-smiles-bindingDB](https://huggingface.co/nepp1d0/prot_bert-finetuned-smiles-bindingDB) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6847
- Accuracy: 0.86
- F1: 0.9247
- Precision: 1.0
- Recall: 0.86
## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6929 | 1.0 | 25 | 0.6847 | 0.86 | 0.9247 | 1.0 | 0.86 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Alfred5347/aaa | Alfred5347 | 2022-12-17T16:47:13Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2022-12-17T16:47:12Z | ---
license: bigscience-openrail-m
---
|
glenn2/q-FrozenLake-v1-4x4-noSlippery | glenn2 | 2022-12-17T16:25:51Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T16:25:45Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="glenn2/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"])
```
|
polejowska/swin-tiny-patch4-window7-224-eurosat | polejowska | 2022-12-17T16:13:17Z | 59 | 0 | transformers | [
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-12-11T12:51:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9851851851851852
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0447
- Accuracy: 0.9852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1547 | 0.99 | 147 | 0.0956 | 0.9711 |
| 0.0707 | 1.99 | 294 | 0.0759 | 0.9733 |
| 0.0537 | 2.99 | 441 | 0.0680 | 0.9768 |
| 0.0302 | 3.99 | 588 | 0.0447 | 0.9852 |
| 0.0225 | 4.99 | 735 | 0.0489 | 0.9837 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
kontogiorgos/testpyramidsrnd | kontogiorgos | 2022-12-17T16:12:41Z | 7 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2022-12-17T16:12:34Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: kontogiorgos/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
troesy/toxicBERT-params-tryout | troesy | 2022-12-17T16:01:36Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-17T15:46:48Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: toxicBERT-params-tryout
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. -->
# toxicBERT-params-tryout
This model is a fine-tuned version of [unitary/toxic-bert](https://huggingface.co/unitary/toxic-bert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1804
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 22 | 0.4522 | 0.0 | 0.0 | 0.0 | 0.8795 |
| No log | 2.0 | 44 | 0.2784 | 0.0 | 0.0 | 0.0 | 0.8996 |
| No log | 3.0 | 66 | 0.2150 | 0.0 | 0.0 | 0.0 | 0.9219 |
| No log | 4.0 | 88 | 0.1888 | 0.0 | 0.0 | 0.0 | 0.9297 |
| No log | 5.0 | 110 | 0.1829 | 0.0 | 0.0 | 0.0 | 0.9303 |
| No log | 6.0 | 132 | 0.1810 | 0.0 | 0.0 | 0.0 | 0.9305 |
| No log | 7.0 | 154 | 0.1804 | 0.0 | 0.0 | 0.0 | 0.9314 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
zigg-ai/d5cf4e49-bdba-434c-b8ed-1efdb5941486 | zigg-ai | 2022-12-17T15:44:38Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-17T15:26:37Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: sdcid
---
### training params
```json
{
"pretrained_model_name_or_path": "multimodalart/sd-fine-tunable",
"instance_data_dir": "./d5cf4e49-bdba-434c-b8ed-1efdb5941486/instance_data",
"class_data_dir": "./class_data/class",
"output_dir": "./d5cf4e49-bdba-434c-b8ed-1efdb5941486/",
"train_text_encoder": true,
"with_prior_preservation": false,
"prior_loss_weight": 1.0,
"instance_prompt": "sdcid",
"class_prompt": "",
"resolution": 512,
"train_batch_size": 1,
"gradient_accumulation_steps": 1,
"gradient_checkpointing": true,
"use_8bit_adam": true,
"learning_rate": 4e-06,
"lr_scheduler": "polynomial",
"lr_warmup_steps": 0,
"num_class_images": 200,
"max_train_steps": 1050,
"mixed_precision": "fp16"
}
```
|
Rubiksman23/ppo-LunarLander-v2 | Rubiksman23 | 2022-12-17T15:43:14Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T15:42:52Z | ---
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: 255.08 +/- 13.11
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
LuniLand/q-Taxi-v3 | LuniLand | 2022-12-17T15:42:46Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T15:42:15Z | ---
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: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="LuniLand/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"])
```
|
SCD28/camembert-ner | SCD28 | 2022-12-17T15:40:39Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-17T12:59:58Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: camembert-ner
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. -->
# camembert-ner
This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1179
- Overall Precision: 0.7367
- Overall Recall: 0.7522
- Overall F1: 0.7444
- Overall Accuracy: 0.9728
- Humanprod F1: 0.1639
- Loc F1: 0.7657
- Org F1: 0.5352
- Per F1: 0.7961
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Humanprod F1 | Loc F1 | Org F1 | Per F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------------:|:------:|:------:|:------:|
| No log | 1.0 | 307 | 0.1254 | 0.7185 | 0.7420 | 0.7300 | 0.9715 | 0.0357 | 0.7579 | 0.5052 | 0.7778 |
| 0.1195 | 2.0 | 614 | 0.1179 | 0.7367 | 0.7522 | 0.7444 | 0.9728 | 0.1639 | 0.7657 | 0.5352 | 0.7961 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.7.1+cpu
- Datasets 2.7.1
- Tokenizers 0.13.2
|
kfahn/Taxi-v3 | kfahn | 2022-12-17T15:23:33Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T15:23:22Z | ---
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="kfahn/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"])
```
|
messham/LunarLander_Course2 | messham | 2022-12-17T15:18:44Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T15:18:17Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.86 +/- 20.76
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Scrya/whisper-medium-ms | Scrya | 2022-12-17T15:08:05Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"ms",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-16T11:39:38Z | ---
language:
- ms
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
model-index:
- name: Whisper Medium MS - FLEURS
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ms_my
split: test
metrics:
- type: wer
value: 11.75
name: WER
- type: cer
value: 3.49
name: CER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium MS - FLEURS
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the FLEURS dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2941
- eval_wer: 10.2
- eval_runtime: 954.9
- eval_samples_per_second: 0.784
- eval_steps_per_second: 0.049
- epoch: 53.2
- step: 5000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 1
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
kfahn/q-FrozenLake-v1 | kfahn | 2022-12-17T15:04:59Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T15:04:48Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1
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="kfahn/q-FrozenLake-v1", 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"])
```
|
marianna13/xlm-roberta-fine-tuned-on-russian-abusive-language | marianna13 | 2022-12-17T14:43:40Z | 38 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"abusive text classification",
"ru",
"en",
"dataset:AbusiveLanguageDataset",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-11T11:59:11Z | ---
language:
- ru
- en
tags:
- abusive text classification
license: "apache-2.0"
datasets:
- AbusiveLanguageDataset
---
```py
from transformers import pipeline
model_path = 'marianna13/xlm-roberta-fine-tuned-on-russian-abusive-language'
id2label = {
0:'неопасный тескт',
1:'опасный тескт'
}
label2id = {
'неопасный тескт':0,
'опасный тескт':1
}
config = AutoConfig.from_pretrained(model_path, id2label=id2label, label2id=label2id)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path, config=config)
text = "Прекрасный день."
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer)
pipe(text)
```
```json
[{'label': 'неопасный текcт', 'score': 0.9249424934387207}]
``` |
anuragshas/whisper-large-v2-hy | anuragshas | 2022-12-17T14:26:43Z | 27 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"hy",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-17T11:27:58Z | ---
language:
- hy
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large-v2 Armenian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 hy-AM
type: mozilla-foundation/common_voice_11_0
config: hy-AM
split: test
args: hy-AM
metrics:
- name: Wer
type: wer
value: 40.23026315789473
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large-v2 Armenian
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 hy-AM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4429
- Wer: 40.2303
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0113 | 8.02 | 200 | 0.3501 | 43.7171 |
| 0.0003 | 17.01 | 400 | 0.3989 | 40.7895 |
| 0.0001 | 26.0 | 600 | 0.4282 | 40.4605 |
| 0.0001 | 34.02 | 800 | 0.4392 | 40.2632 |
| 0.0001 | 43.01 | 1000 | 0.4429 | 40.2303 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
thiagoabreulima/lunarlander | thiagoabreulima | 2022-12-17T14:23:11Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T14:21:58Z | ---
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: 141.70 +/- 56.56
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
...
```
|
gabrielgcbs/dqn-SpaceInvadersNoFrameskip-v4 | gabrielgcbs | 2022-12-17T13:51:25Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-17T13:50:54Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 374.00 +/- 214.89
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga gabrielgcbs -f logs/
python enjoy.py --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 gabrielgcbs -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --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 gabrielgcbs
```
## 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', 0.01),
('learning_starts', 100000),
('n_timesteps', 300000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
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