modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-14 00:44:55
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 519
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-07-14 00:44:41
| card
stringlengths 11
1.01M
|
---|---|---|---|---|---|---|---|---|---|
muhtasham/whisper-medium-tg_tj | muhtasham | 2022-12-20T15:26:32Z | 23 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"hf-asr-leaderboard",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-08T16:31:29Z | ---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium Tajik
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: tg_tj
split: test
args: tg_tj
metrics:
- name: Wer
type: wer
value: 23.153018764230197
---
<!-- 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 Tajik
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs tg_tj dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9217
- Wer: 23.1530
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0016 | 66.0 | 1000 | 0.6929 | 24.2993 |
| 0.0001 | 133.0 | 2000 | 0.8054 | 23.3022 |
| 0.0001 | 199.0 | 3000 | 0.8652 | 23.2237 |
| 0.0 | 266.0 | 4000 | 0.9019 | 23.2394 |
| 0.0 | 333.0 | 5000 | 0.9217 | 23.1530 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
daripaez/q-Taxi-v3 | daripaez | 2022-12-20T15:16:15Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T15:16:07Z | ---
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="daripaez/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"])
```
|
fuatenginoruc/ppo-Huggy | fuatenginoruc | 2022-12-20T14:53:04Z | 12 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-20T14:52:53Z |
---
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: fuatenginoruc/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
wooihen/ppo-Huggy | wooihen | 2022-12-20T14:43:29Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-20T14:42:53Z |
---
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: wooihen/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
daripaez/q-FrozenLake-v1-4x4-noSlippery | daripaez | 2022-12-20T14:41:30Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T14:41:26Z | ---
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="daripaez/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"])
```
|
kzipa/taxi | kzipa | 2022-12-20T14:35:09Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T14:34:57Z | ---
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="kzipa/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"])
```
|
Tavakoli/whisper-small-fa | Tavakoli | 2022-12-20T14:22:36Z | 26 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"fa",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-18T09:58:17Z | ---
language:
- fa
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small fa - ai_farsi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: es
split: test
args: es
metrics:
- name: Wer
type: wer
value: 10.073919143629183
---
<!-- 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 fa - ai_farsi
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: 0.2688
- Wer: 10.0739
## 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2607 | 0.2 | 1000 | 0.3368 | 13.1593 |
| 0.2811 | 0.4 | 2000 | 0.3133 | 12.1733 |
| 0.2166 | 0.6 | 3000 | 0.2933 | 10.9878 |
| 0.2647 | 0.8 | 4000 | 0.2776 | 10.5254 |
| 0.2402 | 1.0 | 5000 | 0.2688 | 10.0739 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
RayKau/taxi | RayKau | 2022-12-20T14:12:37Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T14:12:30Z | ---
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: 7.50 +/- 2.78
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="RayKau/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"])
```
|
jessietextstan/few-shot-model | jessietextstan | 2022-12-20T14:11:11Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-20T14:10:59Z | ---
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 40 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": 1,
"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": 40,
"warmup_steps": 4,
"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 --> |
RayKau/q-FrozenLake-v1-4x4-noSlippery | RayKau | 2022-12-20T14:09:57Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T14:09:50Z | ---
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="RayKau/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"])
```
|
dalist/speechal | dalist | 2022-12-20T14:07:01Z | 0 | 0 | null | [
"whisper-event",
"region:us"
]
| null | 2022-12-20T13:08:48Z | ---
title: Whisper Demo
emoji: π€«
colorFrom: indigo
colorTo: red
sdk: gradio
sdk_version: 3.9.1
app_file: app.py
pinned: false
tags:
- whisper-event
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
DanielViniciusAlves/outputs | DanielViniciusAlves | 2022-12-20T13:56:05Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T09:27:18Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1299
- F1: 0.7010
## 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: 8e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 23 | 0.2190 | 0.7611 |
| No log | 2.0 | 46 | 0.1212 | 0.2309 |
| No log | 3.0 | 69 | 0.1235 | 0.6229 |
| No log | 4.0 | 92 | 0.1299 | 0.7010 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
xmzhu/whisper-small-zh | xmzhu | 2022-12-20T13:54:29Z | 22 | 6 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"zh",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T22:04:01Z | ---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Chinese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 zh-CN
type: mozilla-foundation/common_voice_11_0
config: zh-CN
split: test
args: zh-CN
metrics:
- name: Wer
type: wer
value: 72.36255572065379
---
<!-- 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 Chinese
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 zh-CN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3946
- Wer: 72.3626
## 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5179 | 2.02 | 1000 | 0.3333 | 72.9831 |
| 0.1273 | 4.04 | 2000 | 0.3562 | 73.9621 |
| 0.0163 | 6.06 | 3000 | 0.3790 | 73.9708 |
| 0.004 | 8.07 | 4000 | 0.3946 | 72.3626 |
| 0.025 | 11.0 | 5000 | 0.4019 | 72.6772 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
hedronstone/whisper-large-v2-sw | hedronstone | 2022-12-20T13:53:16Z | 75 | 1 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"sw",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-09T14:47:18Z | ---
language:
- sw
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-large-v2-sw
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sw
split: test
args: 'config: sw, split: test'
metrics:
- name: Wer
type: wer
value: 30.7
---
## Model
* Name: Whisper Large-v2 Swahili
* Description: Whisper weights for speech-to-text task, fine-tuned and evaluated on normalized data.
* Dataset:
- Train and validation splits for Swahili subsets of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0).
- Train, validation and test splits for Swahili subsets of [Google Fleurs](https://huggingface.co/datasets/google/fleurs/).
* Performance: **30.7 WER**
## Weights
* Date of release: 12.09.2022
* License: MIT
## Usage
To use these weights in HuggingFace's `transformers` library, you can do the following:
```python
from transformers import WhisperForConditionalGeneration
model = WhisperForConditionalGeneration.from_pretrained("hedronstone/whisper-large-v2-sw")
```
|
hedronstone/whisper-medium-sw | hedronstone | 2022-12-20T13:52:49Z | 7 | 1 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"sw",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-10T17:06:05Z | ---
language:
- sw
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-medium-sw
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: sw
split: test
args: 'config: sw, split: test'
metrics:
- name: Wer
type: wer
value: 30.51
---
## Model
* Name: Whisper Medium Swahili
* Description: Whisper weights for speech-to-text task, fine-tuned and evaluated on normalized data.
* Performance: **30.51 WER**
## Weights
* Date of release: 12.09.2022
* License: MIT
## Usage
To use these weights in HuggingFace's `transformers` library, you can do the following:
```python
from transformers import WhisperForConditionalGeneration
model = WhisperForConditionalGeneration.from_pretrained("hedronstone/whisper-small-sw")
``` |
Scrya/whisper-large-v2-cantonese | Scrya | 2022-12-20T13:47:04Z | 161 | 6 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"yue",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T11:45:17Z | ---
language:
- yue
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Large V2 - Cantonese - Augmented
results:
- 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: yue
split: test
metrics:
- type: cer
value: 6.213317142278891
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 Large V2 - Cantonese - Augmented
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 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1828
- Cer: 6.2133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Training:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (train+validation)
Evaluation:
- [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (test)
## Training procedure
Datasets were augmented on-the-fly using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift and TimeStretch transformations at `p=0.3`.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1126 | 1.21 | 200 | 0.1666 | 7.3103 |
| 0.0467 | 2.42 | 400 | 0.1610 | 6.9419 |
| 0.0217 | 3.63 | 600 | 0.1621 | 6.3874 |
| 0.008 | 4.85 | 800 | 0.1699 | 6.3064 |
| 0.0023 | 6.06 | 1000 | 0.1828 | 6.2133 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
Arch4ngel/ppo-BreakoutNoFrameskip-v4 | Arch4ngel | 2022-12-20T13:30:55Z | 5 | 0 | stable-baselines3 | [
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T13:30:17Z | ---
library_name: stable-baselines3
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
metrics:
- type: mean_reward
value: 19.10 +/- 4.83
name: mean_reward
verified: false
---
# **PPO** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **BreakoutNoFrameskip-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 ppo --env BreakoutNoFrameskip-v4 -orga Arch4ngel -f logs/
python enjoy.py --algo ppo --env BreakoutNoFrameskip-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 ppo --env BreakoutNoFrameskip-v4 -orga Arch4ngel -f logs/
rl_zoo3 enjoy --algo ppo --env BreakoutNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env BreakoutNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env BreakoutNoFrameskip-v4 -f logs/ -orga Arch4ngel
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 'lin_0.1'),
('ent_coef', 0.01),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('frame_stack', 4),
('learning_rate', 'lin_2.5e-4'),
('n_envs', 8),
('n_epochs', 4),
('n_steps', 128),
('n_timesteps', 1000000.0),
('policy', 'CnnPolicy'),
('vf_coef', 0.5),
('normalize', False)])
```
|
andge/q-Taxi-v3 | andge | 2022-12-20T13:26:23Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T13:26:17Z | ---
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="andge/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"])
```
|
gobbledegook/t5-small-lm-adapt-quotes | gobbledegook | 2022-12-20T13:08:43Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-19T13:40:59Z | * Must prefix prompts with `"write: "`, as in `"write: Today is a great day"`.
* Finetuned from t5-small-lm-adapt. |
hrishikeshagi/imageclassifier | hrishikeshagi | 2022-12-20T12:45:27Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-20T12:30:08Z | # -*- coding: utf-8 -*-
"""imageClassifier.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1S-yO7cqOfeKz8Iu1h-DwhTgkkGml0tKb
"""
pip install git+https://github.com/huggingface/transformers.git
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'https://www.livechennai.com/businesslistings/News_photo/dosa11218.jpg'
image = Image.open(requests.get(url, stream=True).raw)
display(image)
feature_extractor = ViTFeatureExtractor.from_pretrained("Amrrs/south-indian-foods")
model = ViTForImageClassification.from_pretrained("Amrrs/south-indian-foods")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
|
Jingmiao/whisper-small-chineseBaseTW | Jingmiao | 2022-12-20T12:39:27Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"zh",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-20T00:49:33Z | ---
language:
- zh
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small TW on Chinese base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 zh-TW
type: mozilla-foundation/common_voice_11_0
config: zh-TW
split: test
args: zh-TW
metrics:
- name: Wer
type: wer
value: 41.90378710337769
---
<!-- 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 TW on Chinese base
This model is a fine-tuned version of [Jingmiao/whisper-small-chinese_base](https://huggingface.co/Jingmiao/whisper-small-chinese_base) on the mozilla-foundation/common_voice_11_0 zh-TW dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2601
- Wer: 41.9038
## 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0071 | 6.02 | 1000 | 0.2364 | 42.6407 |
| 0.0008 | 13.02 | 2000 | 0.2601 | 41.9038 |
| 0.0004 | 20.01 | 3000 | 0.2771 | 42.3951 |
| 0.0003 | 27.0 | 4000 | 0.2867 | 42.6407 |
| 0.0002 | 33.02 | 5000 | 0.2901 | 42.6407 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
anuragshas/whisper-large-v2-ne | anuragshas | 2022-12-20T12:33:07Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"ne",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-20T09:21:28Z | ---
language:
- ne
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 Nepali
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 ne-NP
type: mozilla-foundation/common_voice_11_0
config: ne-NP
split: test
args: ne-NP
metrics:
- name: Wer
type: wer
value: 56.09756097560976
---
<!-- 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 Nepali
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 ne-NP dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5723
- Wer: 56.0976
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0 | 999.0 | 1000 | 1.5723 | 56.0976 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
kzipa/ppo-Huggy | kzipa | 2022-12-20T12:22:26Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-20T12:22:17Z |
---
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: kzipa/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
emilios/whisper-sm-farsipal-e5 | emilios | 2022-12-20T12:14:20Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"el",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T22:10:13Z | ---
language:
- el
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 small Greek Farsipal and El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr
type: mozilla-foundation/common_voice_11_0,google/fleurs
config: el
split: None
metrics:
- name: Wer
type: wer
value: 17.199108469539375
---
<!-- 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 Greek Farsioal and El Greco
This model is a fine-tuned version of [emilios/whisper-sm-el-farsipal-e4](https://huggingface.co/emilios/whisper-sm-el-farsipal-e4) on the mozilla-foundation/common_voice_11_0,google/fleurs el,el_gr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4871
- Wer: 17.1991
## 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: 500
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1259 | 2.49 | 1000 | 0.4834 | 18.3692 |
| 0.1002 | 4.49 | 2000 | 0.4604 | 17.8027 |
| 0.1096 | 6.98 | 3000 | 0.4553 | 17.8770 |
| 0.0885 | 9.46 | 4000 | 0.4551 | 17.9606 |
| 0.0675 | 11.95 | 5000 | 0.4631 | 17.9049 |
| 0.0675 | 14.44 | 6000 | 0.4619 | 17.9049 |
| 0.0645 | 16.93 | 7000 | 0.4678 | 17.6727 |
| 0.0535 | 19.41 | 8000 | 0.4685 | 17.6634 |
| 0.039 | 21.49 | 9000 | 0.4746 | 17.6727 |
| 0.0447 | 23.98 | 10000 | 0.4761 | 17.6634 |
| 0.0393 | 26.46 | 11000 | 0.4792 | 17.7656 |
| 0.0308 | 28.95 | 12000 | 0.4851 | 17.8678 |
| 0.0301 | 31.44 | 13000 | 0.4846 | 17.4499 |
| 0.031 | 33.93 | 14000 | 0.4849 | 17.8306 |
| 0.0263 | 36.41 | 15000 | 0.4880 | 17.6170 |
| 0.0256 | 38.9 | 16000 | 0.4871 | 17.1991 |
| 0.0236 | 41.39 | 17000 | 0.4883 | 17.2641 |
| 0.0195 | 43.88 | 18000 | 0.4880 | 17.5706 |
| 0.0193 | 46.36 | 19000 | 0.4993 | 17.7285 |
| 0.0161 | 48.85 | 20000 | 0.4968 | 17.8306 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
rmartinshort/ddpm-celebahq-finetuned-butterflies-2epochs | rmartinshort | 2022-12-20T11:33:18Z | 1 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2022-12-20T11:33:01Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class π§¨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('rmartinshort/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
sryu1/dqn-CartPole-v1 | sryu1 | 2022-12-20T11:18:25Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T11:17:57Z | ---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **DQN** Agent playing **CartPole-v1**
This is a trained model of a **DQN** agent playing **CartPole-v1**
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 CartPole-v1 -orga sryu1 -f logs/
python enjoy.py --algo dqn --env CartPole-v1 -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 CartPole-v1 -orga sryu1 -f logs/
rl_zoo3 enjoy --algo dqn --env CartPole-v1 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env CartPole-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env CartPole-v1 -f logs/ -orga sryu1
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('exploration_final_eps', 0.04),
('exploration_fraction', 0.16),
('gamma', 0.99),
('gradient_steps', 128),
('learning_rate', 0.0023),
('learning_starts', 1000),
('n_timesteps', 50000.0),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[256, 256])'),
('target_update_interval', 10),
('train_freq', 256),
('normalize', False)])
```
|
SiddhantKadwe/ppo-LunarLander-v2-TEST | SiddhantKadwe | 2022-12-20T11:06:57Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T10:40:55Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.02 +/- 23.15
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
...
```
|
cahya/whisper-large-id | cahya | 2022-12-20T10:22:52Z | 137 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"id",
"dataset:mozilla-foundation/common_voice_11_0",
"dataset:magic_data",
"dataset:TITML",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-08T10:09:52Z | ---
language:
- id
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- magic_data
- TITML
metrics:
- wer
model-index:
- name: Whisper Large Indonesian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 id
type: mozilla-foundation/common_voice_11_0
config: id
split: test
metrics:
- name: Wer
type: wer
value: 6.248270773771097
---
<!-- 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 Indonesian
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0, magic_data, titml id dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2034
- Wer: 6.2483
## 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: 12
- eval_batch_size: 12
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1516 | 0.5 | 1000 | 0.1730 | 6.5664 |
| 0.1081 | 1.0 | 2000 | 0.1638 | 6.3682 |
| 0.0715 | 1.49 | 3000 | 0.1803 | 6.2713 |
| 0.1009 | 1.99 | 4000 | 0.1796 | 6.2667 |
| 0.0387 | 2.49 | 5000 | 0.2054 | 6.4927 |
| 0.0494 | 2.99 | 6000 | 0.2034 | 6.2483 |
| 0.0259 | 3.48 | 7000 | 0.2226 | 6.3497 |
| 0.0265 | 3.98 | 8000 | 0.2274 | 6.4004 |
| 0.0232 | 4.48 | 9000 | 0.2443 | 6.5618 |
| 0.015 | 4.98 | 10000 | 0.2413 | 6.4927 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
teacookies/autotrain-20-12-2022_exam_part3-2543877946 | teacookies | 2022-12-20T10:12:28Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"unk",
"dataset:teacookies/autotrain-data-20-12-2022_exam_part3",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-20T10:00:56Z | ---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain π€"
datasets:
- teacookies/autotrain-data-20-12-2022_exam_part3
co2_eq_emissions:
emissions: 21.733051144065605
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 2543877946
- CO2 Emissions (in grams): 21.7331
## Validation Metrics
- Loss: 0.010
- Accuracy: 0.998
- Precision: 0.739
- Recall: 0.786
- F1: 0.762
## 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/teacookies/autotrain-20-12-2022_exam_part3-2543877946
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-20-12-2022_exam_part3-2543877946", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-20-12-2022_exam_part3-2543877946", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
spaablauw/HyperNuke | spaablauw | 2022-12-20T10:01:04Z | 0 | 7 | null | [
"license:wtfpl",
"region:us"
]
| null | 2022-12-20T09:53:21Z | ---
license: wtfpl
---
Doesn't always work, but gets you a lot closer quickly compared to just trying keywords.
Dataset of 18 images, 3 vectors, batch size of 3, 3 steps gradient accumulation, trained for 500 steps or 250 epochs.




|
fcakyon/yolov5n-cls-v7.0 | fcakyon | 2022-12-20T09:56:24Z | 20 | 2 | transformers | [
"transformers",
"image-classification",
"computer-vision",
"vision",
"yolo",
"yolov5",
"license:gpl-3.0",
"region:us"
]
| image-classification | 2022-12-13T22:42:21Z | ---
license: gpl-3.0
inference: false
tags:
- image-classification
- computer-vision
- vision
- yolo
- yolov5
---
### How to use
- Install yolov5:
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('fcakyon/yolov5n-cls-v7.0')
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img)
```
- Finetune the model on your custom dataset:
```bash
yolov5 classify train --img 128 --data mnist2560 --model fcakyon/yolov5n-cls-v7.0 --epochs 1 --device cpu
``` |
fcakyon/yolov5n-seg-v7.0 | fcakyon | 2022-12-20T09:55:41Z | 2 | 0 | transformers | [
"transformers",
"instance-segmentation",
"computer-vision",
"vision",
"yolo",
"yolov5",
"license:gpl-3.0",
"region:us"
]
| null | 2022-12-13T22:51:17Z | ---
license: gpl-3.0
inference: false
tags:
- instance-segmentation
- computer-vision
- vision
- yolo
- yolov5
---
### How to use
- Install yolov5:
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('fcakyon/yolov5n-seg-v7.0')
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img)
```
- Finetune the model on your custom dataset:
```bash
yolov5 segment train --img 128 --weights fcakyon/yolov5n-seg-v7.0 --epochs 1 --device cpu
``` |
IceKingBing/distilbert-imdb | IceKingBing | 2022-12-20T09:52:44Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-20T08:07:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 391 | 0.1847 | 0.9291 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.12.0+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
fcakyon/yolov5s-v7.0 | fcakyon | 2022-12-20T09:51:11Z | 65 | 13 | transformers | [
"transformers",
"object-detection",
"computer-vision",
"vision",
"yolo",
"yolov5",
"dataset:detection-datasets/coco",
"license:gpl-3.0",
"region:us"
]
| object-detection | 2022-12-13T21:26:21Z | ---
license: gpl-3.0
inference: false
tags:
- object-detection
- computer-vision
- vision
- yolo
- yolov5
datasets:
- detection-datasets/coco
---
### How to use
- Install yolov5:
```bash
pip install -U yolov5
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('fcakyon/yolov5s-v7.0')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.max_det = 1000 # maximum number of detections per image
# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model(img)
# inference with larger input size
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --img 640 --batch 16 --weights fcakyon/yolov5s-v7.0 --epochs 10 --device cuda:0
``` |
IngoTB303/q-Taxi-v3 | IngoTB303 | 2022-12-20T09:41:46Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T09:22:38Z | ---
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: 7.56 +/- 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="IngoTB303/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"])
```
|
Gabriel/bart-base-cnn-swe | Gabriel | 2022-12-20T09:37:08Z | 50 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"sv",
"dataset:Gabriel/cnn_daily_swe",
"license:mit",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2022-08-26T05:26:14Z | ---
language: sv
license: mit
tags:
- summarization
datasets:
- Gabriel/cnn_daily_swe
widget:
- text: 'Frankrike lΓ₯s Sebastien Chabal har nΓ€mnts fΓΆr en farlig tackling pΓ₯ Englands
Simon Shaw under lΓΆrdagens VM semifinal i Paris. Simon Shaw lastar av trots att
Raphael Ibanez, vΓ€nster, och Sebastien Chabal. Sale Sharks framΓ₯t kommer att stΓ€llas
infΓΆr en disciplinΓ€r utfrΓ₯gning pΓ₯ mΓ₯ndag efter hans tackling pΓ₯ motsatt andra-rower
Shaw noterades genom att citera kommissionΓ€r Dennis Wheelahan. Chabal bΓΆrjade
matchen pΓ₯ ersΓ€ttningsbΓ€nken, men kom i 26: e minuten att ersΓ€tta den skadade
Fabien Pelous under vΓ€rd Frankrikes 14-9 nederlag. Om han blir avstΓ€ngd missar
Chabal fredagens tredje och fjΓ€rde match pΓ₯ Parc des Princes. Samtidigt, Frankrike
trΓ€nare Bernard Laporte sade att nederlaget var svΓ₯rare att ta Γ€n Englands 24-7
seger i 2003 semifinalen. "Γ
r 2003 var de bΓ€ttre Γ€n oss. I sjΓ€lva verket var de
bΓ€ttre Γ€n alla", sade Laporte, som lΓ€mnar sin roll att tilltrΓ€da posten som junior
idrottsminister i den franska regeringen. "De var som Nya Zeeland i denna turnering
- favoriten, fΓΆrutom att de gick hela vΓ€gen. Den hΓ€r gΓ₯ngen Γ€r det svΓ₯rare fΓΆr
igΓ₯r var det 50-50." Samtidigt, England -- fΓΆrsΓΆker bli den fΓΆrsta nationen att
fΓΆrsvara VM-titeln -- avslΓΆjade att stjΓ€rna kicker Jonny Wilkinson Γ₯terigen hade
problem med matchbollarna under semifinalen. Flughalvan, som uttryckte sin oro
efter att ha kΓ€mpat med stΓΆveln mot Australien, avvisade en boll innan han sparkade
en vital trepoΓ€ngare mot Frankrike. "Vi sa det inte fΓΆrra veckan men en icke-match
bollen kom ut pΓ₯ fΓ€ltet i Marseille som Jonny sparkade," chef fΓΆr rugby Rob Andrew
sade. "Han tΓ€nkte inte pΓ₯ det nΓ€r han sparkade det. Matchbollarna Γ€r mΓ€rkta, numrerade
ett till sex. IgΓ₯r kvΓ€ll hade de "World Cup semifinal England vs Frankrike" skrivet
pΓ₯ dem. PΓ₯ matchkvΓ€llen var Jonny vaksam nΓ€r han sparkade fΓΆr mΓ₯l att de faktiskt
var matchbollar han sparkade. "TrΓ€ningsbollarna fΓΆrlorar tryck och form. Hela
frΓ₯gan fΓΆrra veckan, arrangΓΆrerna accepterade alla sex matchbollar bΓΆr anvΓ€ndas
av bΓ₯da sidor pΓ₯ torsdagen fΓΆre matchen. " E-post till en vΓ€n.'
inference:
parameters:
temperature: 0.7
min_length: 30
max_length: 120
train-eval-index:
- config: Gabriel--xsum_swe
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
document: text
summary: target
co2_eq_emissions:
emissions: 0.0334
source: Google Colab
training_type: fine-tuning
geographical_location: Fredericia, Denmark
hardware_used: Tesla P100-PCIE-16GB
model-index:
- name: bart-base-cnn-swe
results:
- task:
type: summarization
name: summarization
dataset:
name: Gabriel/cnn_daily_swe
type: Gabriel/cnn_daily_swe
split: validation
metrics:
- type: rouge-1
value: 22.2046
name: Validation ROGUE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2U4YjhhNzhjYmQ2ODY2YWU1NzY5Y2RkYzIzNTUxOTA0MmUwZjNmYTdjNmE5OWNkODhhYWExOGNkOGJkNTBkZiIsInZlcnNpb24iOjF9.lEMnUJOeoa5LepNmjsRflkQmtJAaVo03ocSs9JJuxbLeu4x0oY-XsML3O1IfDJQuvlO4WHZviykRLabkhTtbBQ
- type: rouge-2
value: 10.4332
name: Validation ROGUE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODkzOGYxNTNiMjc1MGIyODAzY2Q2MjA0NTg1N2NjODc5YWZlZGI5Y2Q3ZDAwNTQyMjA4MmJjOGUxYzVlOGFlNyIsInZlcnNpb24iOjF9.lpn8VP1_AvHXvyYDJHEfMoIpb-_B5fXvMaMQ249Tngo3HBPrexPmhEvGqj1HVnVGxVFyDFhF2tYh4AhThguoBQ
- type: rouge-l
value: 18.1753
name: Validation ROGUE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWRkM2ViZjRmYmNkZTI1NWE1ODUzNGMwZTYzNzU1Yjk1ODM3NDNkZWMwMjc1ZGJmZGZkNWQxYThmN2ZiZDhjZCIsInZlcnNpb24iOjF9.6ENOPrpZ245V0jcZtiSOmWwdr06W9prPAyE9Qjnn5meiE7yoc0T0oquE9d8SLOfFqYcIbCb6vVUSVxFewj_VAA
- type: rouge-l-sum
value: 20.846
name: Validation ROGUE-L-SUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDJjODc0MTQwYTM5OGRmMjE2ODUxNGM4YmYxMTJlNDE1MzI0M2Q2ZGJkZDlkOWE2NTMxNjI0YjZjMDQwYjNjNyIsInZlcnNpb24iOjF9.FvNGRVWTCJSafucQKp3eW1B__SAHCL7qHBzooe8ufYIijnNuope0W7AIphex1WMT_9o_Unni2vPGFUvT2o9qAg
---
# bart-base-cnn-swe
This model is a W.I.P
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. This model is a fine-tuned version of [KBLab/bart-base-swedish-cased](https://huggingface.co/KBLab/bart-base-swedish-cased) on the [Gabriel/bart-base-cnn-swe](https://huggingface.co/datasets/Gabriel/cnn_daily_swe) dataset and can be used for summarization tasks.
## Intended uses & limitations
This model should only be used to fine-tune further on and summarization tasks.
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="Gabriel/bart-base-cnn-swe")
ARTICLE = """
Frankrike lΓ₯s Sebastien Chabal har nΓ€mnts fΓΆr en farlig tackling pΓ₯ Englands Simon Shaw under lΓΆrdagens VM semifinal i Paris. Simon Shaw lastar av trots att Raphael Ibanez, vΓ€nster, och Sebastien Chabal. Sale Sharks framΓ₯t kommer att stΓ€llas infΓΆr en disciplinΓ€r utfrΓ₯gning pΓ₯ mΓ₯ndag efter hans tackling pΓ₯ motsatt andra-rower Shaw noterades genom att citera kommissionΓ€r Dennis Wheelahan. Chabal bΓΆrjade matchen pΓ₯ ersΓ€ttningsbΓ€nken, men kom i 26: e minuten att ersΓ€tta den skadade Fabien Pelous under vΓ€rd Frankrikes 14-9 nederlag. Om han blir avstΓ€ngd missar Chabal fredagens tredje och fjΓ€rde match pΓ₯ Parc des Princes. Samtidigt, Frankrike trΓ€nare Bernard Laporte sade att nederlaget var svΓ₯rare att ta Γ€n Englands 24-7 seger i 2003 semifinalen. "Γ
r 2003 var de bΓ€ttre Γ€n oss. I sjΓ€lva verket var de bΓ€ttre Γ€n alla", sade Laporte, som lΓ€mnar sin roll att tilltrΓ€da posten som junior idrottsminister i den franska regeringen. "De var som Nya Zeeland i denna turnering - favoriten, fΓΆrutom att de gick hela vΓ€gen. Den hΓ€r gΓ₯ngen Γ€r det svΓ₯rare fΓΆr igΓ₯r var det 50-50." Samtidigt, England -- fΓΆrsΓΆker bli den fΓΆrsta nationen att fΓΆrsvara VM-titeln -- avslΓΆjade att stjΓ€rna kicker Jonny Wilkinson Γ₯terigen hade problem med matchbollarna under semifinalen. Flughalvan, som uttryckte sin oro efter att ha kΓ€mpat med stΓΆveln mot Australien, avvisade en boll innan han sparkade en vital trepoΓ€ngare mot Frankrike. "Vi sa det inte fΓΆrra veckan men en icke-match bollen kom ut pΓ₯ fΓ€ltet i Marseille som Jonny sparkade," chef fΓΆr rugby Rob Andrew sade. "Han tΓ€nkte inte pΓ₯ det nΓ€r han sparkade det. Matchbollarna Γ€r mΓ€rkta, numrerade ett till sex. IgΓ₯r kvΓ€ll hade de "World Cup semifinal England vs Frankrike" skrivet pΓ₯ dem. PΓ₯ matchkvΓ€llen var Jonny vaksam nΓ€r han sparkade fΓΆr mΓ₯l att de faktiskt var matchbollar han sparkade. "TrΓ€ningsbollarna fΓΆrlorar tryck och form. Hela frΓ₯gan fΓΆrra veckan, arrangΓΆrerna accepterade alla sex matchbollar bΓΆr anvΓ€ndas av bΓ₯da sidor pΓ₯ torsdagen fΓΆre matchen. " E-post till en vΓ€n.
"""
print(summarizer(ARTICLE, max_length=130, min_length=30, num_beams=10 ,do_sample=False))
>>> [{'summary_text': """ Frankrike lΓ₯s Sebastien Chabal har nΓ€mnts fΓΆr en farlig tackling pΓ₯ Englands Simon Shaw under VM semifinal i Paris. Sale Sharks framΓ₯t kommer att stΓ€llas infΓΆr en disciplinΓ€r utfrΓ₯gning pΓ₯ mΓ₯ndag efter hans tackling pΓ₯ motsatt andra - rower Shaw noterades genom att citera kommissionΓ€r Dennis Wheelahan. Om Chabal blir avstΓ€ngd missar Chabal fredagens tredje och fjΓ€rde match pΓ₯ Parc des Princes."""}]
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 2*2 = 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.2349 | 1.0 | 17944 | 2.0643 | 21.9564 | 10.2133 | 17.9958 | 20.6502 | 19.9992 |
| 2.0726 | 2.0 | 35888 | 2.0253 | 22.0568 | 10.3302 | 18.0648 | 20.7482 | 19.9996 |
| 1.8658 | 3.0 | 53832 | 2.0333 | 22.0871 | 10.2902 | 18.0577 | 20.7082 | 19.998 |
| 1.8121 | 4.0 | 71776 | 1.9759 | 22.2046 | 10.4332 | 18.1753 | 20.846 | 19.9971 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
tmsreekanth98/keyphrase-extractions_fmlm | tmsreekanth98 | 2022-12-20T09:26:27Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:kp20k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-20T05:33:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- kp20k
model-index:
- name: keyphrase-extractions_fmlm
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. -->
# keyphrase-extractions_fmlm
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the kp20k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3347
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3947 | 1.0 | 2125 | 0.3471 |
| 0.3651 | 2.0 | 4250 | 0.3320 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
anuragshas/whisper-large-v2-ha | anuragshas | 2022-12-20T09:26:08Z | 8 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"ha",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-20T07:05:26Z | ---
language:
- ha
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 Hausa
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 ha
type: mozilla-foundation/common_voice_11_0
config: ha
split: test
args: ha
metrics:
- name: Wer
type: wer
value: 37.406890130353815
---
<!-- 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 Hausa
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 ha dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8247
- Wer: 37.4069
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0014 | 12.06 | 1000 | 0.8247 | 37.4069 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
teacookies/autotrain-20-12-2022_general_info_exam-2543777917 | teacookies | 2022-12-20T09:22:01Z | 19 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"unk",
"dataset:teacookies/autotrain-data-20-12-2022_general_info_exam",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-20T09:10:26Z | ---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain π€"
datasets:
- teacookies/autotrain-data-20-12-2022_general_info_exam
co2_eq_emissions:
emissions: 21.038593211432406
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 2543777917
- CO2 Emissions (in grams): 21.0386
## Validation Metrics
- Loss: 0.007
- Accuracy: 0.998
- Precision: 0.937
- Recall: 0.952
- 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/teacookies/autotrain-20-12-2022_general_info_exam-2543777917
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-20-12-2022_general_info_exam-2543777917", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-20-12-2022_general_info_exam-2543777917", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
waynedsouza/only_names_fullt | waynedsouza | 2022-12-20T09:20:42Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-20T08:03:15Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# waynedsouza/only_names_fullt
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('waynedsouza/only_names_fullt')
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=waynedsouza/only_names_fullt)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1790 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": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
sipheiroce/dqn-SpaceInvadersNoFrameskip-v4 | sipheiroce | 2022-12-20T08:42:29Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T08:41:53Z | ---
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: 408.50 +/- 127.91
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 sipheiroce -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 sipheiroce -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 sipheiroce
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 300000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Cwhgn/DAMO-YOLO-S | Cwhgn | 2022-12-20T08:15:05Z | 0 | 1 | null | [
"arxiv:2211.15444",
"license:apache-2.0",
"region:us"
]
| null | 2022-12-20T08:12:30Z | ---
license: apache-2.0
---
## Model Description
This **DAMO-YOLO-S** model is a small-size object detection model with fast inference speed and high accuracy, trained by **DAMO-YOLO**.
DAMO-YOLO is a fast and accurate object detection method, which is developed by TinyML Team from Alibaba DAMO Data Analytics and Intelligence Lab. And it achieves a higher performance than state-of-the-art YOLO series. DAMO-YOLO is extend from YOLO but with some new techs, including Neural Architecture Search (NAS) backbones, efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. For more details, please refer to our [Arxiv Report](https://arxiv.org/abs/2211.15444) and [Github Code](https://github.com/tinyvision/DAMO-YOLO). Moreover, here you can find not only powerful models, but also highly efficient training strategies and complete tools from training to deployment.
## Chinese Web Demo
- We also provide Chinese Web Demo on ModelScope, including [DAMO-YOLO-T](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-t/summary), [DAMO-YOLO-S](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo/summary), [DAMO-YOLO-M](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-m/summary).
## Datasets
The model is trained on COCO2017.
## Model Usage
The usage guideline can be found in our [Quick Start Tutorial](https://github.com/tinyvision/DAMO-YOLO).
## Model Evaluation
|Model |size |mAP<sup>val<br>0.5:0.95 | Latency T4<br>TRT-FP16-BS1| FLOPs<br>(G)| Params<br>(M)| Download |
| ------ |:---: | :---: |:---:|:---: | :---: | :---:|
|[DAMO-YOLO-T](./configs/damoyolo_tinynasL20_T.py) | 640 | 41.8 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL20_T_418.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL20_T_418.onnx) |
|[DAMO-YOLO-T*](./configs/damoyolo_tinynasL20_T.py) | 640 | 43.0 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL20_T.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL20_T.onnx) |
|[DAMO-YOLO-S](./configs/damoyolo_tinynasL25_S.py) | 640 | 45.6 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL25_S_456.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL25_S_456.onnx) |
|[DAMO-YOLO-S*](./configs/damoyolo_tinynasL25_S.py) | 640 | 46.8 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL25_S.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL25_S.onnx) |
|[DAMO-YOLO-M](./configs/damoyolo_tinynasL35_M.py) | 640 | 48.7 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL35_M_487.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL35_M_487.onnx)|
|[DAMO-YOLO-M*](./configs/damoyolo_tinynasL35_M.py) | 640 | 50.0 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL35_M.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL35_M.onnx)|
- We report the mAP of models on COCO2017 validation set, with multi-class NMS.
- The latency in this table is measured without post-processing.
- \* denotes the model trained with distillation.
## Cite DAMO-YOLO
If you use DAMO-YOLO in your research, please cite our work by using the following BibTeX entry:
```latex
@article{damoyolo,
title={DAMO-YOLO: A Report on Real-Time Object Detection Design},
author={Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang and Xiuyu Sun},
journal={arXiv preprint arXiv:2211.15444v2},
year={2022},
}
```
|
Cwhgn/DAMO-YOLO-T | Cwhgn | 2022-12-20T08:14:56Z | 0 | 1 | null | [
"arxiv:2211.15444",
"license:apache-2.0",
"region:us"
]
| null | 2022-12-20T06:55:12Z | ---
license: apache-2.0
---
## Model Description
This **DAMO-YOLO-T** model is a tiny-size object detection model with fast inference speed and high accuracy, trained by **DAMO-YOLO**.
DAMO-YOLO is a fast and accurate object detection method, which is developed by TinyML Team from Alibaba DAMO Data Analytics and Intelligence Lab. And it achieves a higher performance than state-of-the-art YOLO series. DAMO-YOLO is extend from YOLO but with some new techs, including Neural Architecture Search (NAS) backbones, efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. For more details, please refer to our [Arxiv Report](https://arxiv.org/abs/2211.15444) and [Github Code](https://github.com/tinyvision/DAMO-YOLO). Moreover, here you can find not only powerful models, but also highly efficient training strategies and complete tools from training to deployment.
## Chinese Web Demo
- We also provide Chinese Web Demo on ModelScope, including [DAMO-YOLO-T](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-t/summary), [DAMO-YOLO-S](https://modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo/summary), [DAMO-YOLO-M](https://www.modelscope.cn/models/damo/cv_tinynas_object-detection_damoyolo-m/summary).
## Datasets
The model is trained on COCO2017.
## Model Usage
The usage guideline can be found in our [Quick Start Tutorial](https://github.com/tinyvision/DAMO-YOLO).
## Model Evaluation
|Model |size |mAP<sup>val<br>0.5:0.95 | Latency T4<br>TRT-FP16-BS1| FLOPs<br>(G)| Params<br>(M)| Download |
| ------ |:---: | :---: |:---:|:---: | :---: | :---:|
|[DAMO-YOLO-T](./configs/damoyolo_tinynasL20_T.py) | 640 | 41.8 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL20_T_418.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL20_T_418.onnx) |
|[DAMO-YOLO-T*](./configs/damoyolo_tinynasL20_T.py) | 640 | 43.0 | 2.78 | 18.1 | 8.5 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL20_T.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL20_T.onnx) |
|[DAMO-YOLO-S](./configs/damoyolo_tinynasL25_S.py) | 640 | 45.6 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL25_S_456.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL25_S_456.onnx) |
|[DAMO-YOLO-S*](./configs/damoyolo_tinynasL25_S.py) | 640 | 46.8 | 3.83 | 37.8 | 16.3 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL25_S.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL25_S.onnx) |
|[DAMO-YOLO-M](./configs/damoyolo_tinynasL35_M.py) | 640 | 48.7 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/before_distill/damoyolo_tinynasL35_M_487.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/before_distill/damoyolo_tinynasL35_M_487.onnx)|
|[DAMO-YOLO-M*](./configs/damoyolo_tinynasL35_M.py) | 640 | 50.0 | 5.62 | 61.8 | 28.2 |[torch](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/clean_models/damoyolo_tinynasL35_M.pth),[onnx](https://idstcv.oss-cn-zhangjiakou.aliyuncs.com/DAMO-YOLO/onnx/damoyolo_tinynasL35_M.onnx)|
- We report the mAP of models on COCO2017 validation set, with multi-class NMS.
- The latency in this table is measured without post-processing.
- \* denotes the model trained with distillation.
## Cite DAMO-YOLO
If you use DAMO-YOLO in your research, please cite our work by using the following BibTeX entry:
```latex
@article{damoyolo,
title={DAMO-YOLO: A Report on Real-Time Object Detection Design},
author={Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang and Xiuyu Sun},
journal={arXiv preprint arXiv:2211.15444v2},
year={2022},
}
```
|
aaryavartno1/shiva-new-model | aaryavartno1 | 2022-12-20T07:45:13Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2022-12-20T07:45:02Z | ---
license: bigscience-openrail-m
---
|
ltorrick/sd-class-butterflies-32 | ltorrick | 2022-12-20T07:27:05Z | 0 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2022-12-20T07:26:49Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class π§¨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute π¦.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('ltorrick/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
makitanikaze/P5 | makitanikaze | 2022-12-20T07:22:32Z | 10 | 21 | null | [
"sequential-recommendation",
"direct-recommendation",
"explanation-generation",
"en",
"dataset:amazon_us_reviews",
"dataset:yelp_review_full",
"license:mit",
"region:us"
]
| null | 2022-12-13T09:26:31Z |
---
language:
- en
tags:
- sequential-recommendation
- direct-recommendation
- explanation-generation
license: mit
datasets:
- amazon_us_reviews
- yelp_review_full
metrics:
- NDCG
- HR
- MAE
- BLUE
- ROUGE
---
# P5
Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm

### Models:
[P5 (Sports Small)](https://huggingface.co/makitanikaze/P5_sports_small)
[P5 (Beauty Small)](https://huggingface.co/makitanikaze/P5_beauty_small)
[P5 (Toys Small)](https://huggingface.co/makitanikaze/P5_toys_small)
[P5 (Yelp Small)](https://huggingface.co/makitanikaze/P5_yelp_small)
[P5 (Sports Base)](https://huggingface.co/makitanikaze/P5_sports_base)
[P5 (Beauty Base)](https://huggingface.co/makitanikaze/P5_beauty_base)
[P5 (Toys Base)](https://huggingface.co/makitanikaze/P5_toys_base) |
jxiao/q-taxv3-v1 | jxiao | 2022-12-20T07:21:59Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T06:42:20Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxv3-v1
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="jxiao/q-taxv3-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"])
```
|
saikiranp/ppo-LunarLander-v2-1 | saikiranp | 2022-12-20T07:19:27Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T07:19:07Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.49 +/- 15.47
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
...
```
|
Gozie/ppo-LunarLander-v2 | Gozie | 2022-12-20T07:06:06Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T07:05:43Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SB3 PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 270.67 +/- 20.46
name: mean_reward
verified: false
---
# **SB3 PPO** Agent playing **LunarLander-v2**
This is a trained model of a **SB3 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
...
```
|
makitanikaze/P5_yelp_small | makitanikaze | 2022-12-20T06:54:21Z | 10 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"sequential-recommendation",
"direct-recommendation",
"explanation-generation",
"text2text-generation",
"custom_code",
"en",
"dataset:yelp_review_full",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-20T06:51:11Z |
---
language:
- en
tags:
- sequential-recommendation
- direct-recommendation
- explanation-generation
- text2text-generation
license: mit
datasets:
- yelp_review_full
metrics:
- NDCG
- HR
- MAE
- BLUE
- ROUGE
widget:
- text: "Based on the visit history of Herman : \n 236 -> 4979 -> 3240 -> 2208 -> 168 -> 12101 \n Can you decide the next business likely to be visited by the user ?"
example_title: "Sequential Recommendation"
- text: "We want to make recommendation for user_1618 . Select the best item from these candidates : \n 13978 , 2053 , 19198 , 16484 , 771 , 5354 , 10803 , 18199 , 15098 , 1070 , 7183 , 193 , 13633 , 16465 , 1302 , 14943 , 12902 , 10984 , 18198 , 6062 , 11955 , 6809 , 12601 , 2373 , 14706 , 8889 , 17980 , 17294 , 15002 , 12237 , 13299 , 18856 , 7066 , 11792 , 17093 , 18779 , 19563 , 2615 , 17865 , 11774 , 13562 , 11128 , 14810 , 10149 , 18543 , 12854 , 12508 , 7662 , 7227 , 3058 , 11704 , 1200 , 12439 , 17873 , 16280 , 15225 , 307 , 11428 , 17107 , 4727 , 2030 , 6914 , 8234 , 2174 , 14340 , 17577 , 15342 , 14741 , 19058 , 14694 , 2114 , 15739 , 8739 , 14263 , 4687 , 4977 , 6685 , 381 , 16542 , 19230 , 9977 , 8449 , 18537 , 6616 , 8945 , 12265 , 4836 , 7705 , 19865 , 15843 , 18715 , 12834 , 18955 , 6324 , 4740 , 14717 , 2752 , 2131 , 5957 , 7511"
example_title: "Direct Recommendation"
- text: "Based on the feature word drinks , generate an explanation for user_5657 about this business : Ghost Ranch: Modern Southwest Cuisine"
example_title: "Explanation Generation"
---
# P5 (Yelp Small)
Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm
 |
makitanikaze/P5_toys_small | makitanikaze | 2022-12-20T05:53:37Z | 65 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"feature-extraction",
"sequential-recommendation",
"direct-recommendation",
"explanation-generation",
"text2text-generation",
"custom_code",
"en",
"dataset:amazon_us_reviews",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-20T04:59:28Z |
---
language:
- en
tags:
- sequential-recommendation
- direct-recommendation
- explanation-generation
- text2text-generation
license: mit
datasets:
- amazon_us_reviews
metrics:
- NDCG
- HR
- MAE
- BLUE
- ROUGE
widget:
- text: "According to the purchase history of JSB727 : \n 2440 -> 1212 -> 4234 -> 4309 \n Can you recommend the next possible item to the user ?"
example_title: "Sequential Recommendation"
- text: "We want to make recommendation for user_823 . Select the best item from these candidates : \n 1402 , 6122 , 10240 , 84 , 10691 , 10944 , 3451 , 10213 , 4239 , 9018 , 3384 , 1397 , 7232 , 8221 , 7751 , 5397 , 11253 , 5078 , 3231 , 5105 , 3959 , 7245 , 150 , 2253 , 764 , 2646 , 6909 , 1939 , 1982 , 2201 , 11503 , 11603 , 4050 , 9524 , 1895 , 2317 , 11027 , 6971 , 4441 , 2336 , 373 , 11735 , 3824 , 51 , 9514 , 2488 , 7960 , 7653 , 2017 , 6541 , 3682 , 1064 , 910 , 6117 , 4556 , 3939 , 7049 , 241 , 10113 , 1442 , 10149 , 5930 , 7638 , 1140 , 11589 , 3935 , 11178 , 10491 , 8361 , 11266 , 2029 , 8965 , 4118 , 7506 , 3465 , 4283 , 2812 , 5465 , 2557 , 7510 , 2243 , 10292 , 1212 , 11419 , 3877 , 4771 , 3267 , 8508 , 10200 , 4048 , 3592 , 10145 , 4419 , 9583 , 6193 , 9257 , 1365 , 4672 , 9772 , 11440"
example_title: "Direct Recommendation"
- text: "Based on the feature word kids , generate an explanation for user_12107 about this product : Tumblin' Monkeys Game"
example_title: "Explanation Generation"
---
# P5 (Toys Small)
Recommendation as Language Processing: A Unified Pretrain, Personalized Prompt & Predict Paradigm
 |
WilliamWen/bert-finetuned-ner | WilliamWen | 2022-12-20T05:08:44Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-19T10:05:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9314616019818331
- name: Recall
type: recall
value: 0.9491753618310333
- name: F1
type: f1
value: 0.9402350587646913
- name: Accuracy
type: accuracy
value: 0.9857243774651204
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0636
- Precision: 0.9315
- Recall: 0.9492
- F1: 0.9402
- Accuracy: 0.9857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0898 | 1.0 | 1756 | 0.0718 | 0.9149 | 0.9359 | 0.9253 | 0.9811 |
| 0.0351 | 2.0 | 3512 | 0.0641 | 0.9298 | 0.9490 | 0.9393 | 0.9860 |
| 0.0186 | 3.0 | 5268 | 0.0636 | 0.9315 | 0.9492 | 0.9402 | 0.9857 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
TheBirdLegacy/CatsandDogsPOC-Swin | TheBirdLegacy | 2022-12-20T05:06:59Z | 41 | 0 | transformers | [
"transformers",
"pytorch",
"swin",
"image-classification",
"autotrain",
"vision",
"dataset:puffy310/autotrain-data-synth-cats-or-dogs",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-12-20T04:53:44Z | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- puffy310/autotrain-data-synth-cats-or-dogs
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 1.165641024416945
---
Resnet is more lightweight but this is better in terms of loss, at the cost of being 3.5X the size.
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2540477801
- CO2 Emissions (in grams): 1.1656
## Validation Metrics
- Loss: 0.000
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000 |
sgangireddy/whisper-medium-cv-fi-hu-1k | sgangireddy | 2022-12-20T04:19:07Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T18:28:59Z | ---
license: apache-2.0
tags:
- 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: 0.3236
- Wer: 19.7922
## 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: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0068 | 4.04 | 1000 | 0.3236 | 19.7922 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
teacookies/autotrain-20-12-2022-2540377772 | teacookies | 2022-12-20T04:13:06Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"unk",
"dataset:teacookies/autotrain-data-20-12-2022",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-20T04:05:12Z | ---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain π€"
datasets:
- teacookies/autotrain-data-20-12-2022
co2_eq_emissions:
emissions: 14.305842162020754
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 2540377772
- CO2 Emissions (in grams): 14.3058
## Validation Metrics
- Loss: 0.019
- Accuracy: 0.994
- Precision: 0.804
- Recall: 0.853
- F1: 0.828
## 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/teacookies/autotrain-20-12-2022-2540377772
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-20-12-2022-2540377772", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-20-12-2022-2540377772", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
NickForMe/rubert-base-cased-finetuned-panx-de | NickForMe | 2022-12-20T03:21:17Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:universal_dependencies",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-12-20T03:06:21Z | ---
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- f1
model-index:
- name: rubert-base-cased-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: universal_dependencies
type: universal_dependencies
config: ru_taiga
split: train
args: ru_taiga
metrics:
- name: F1
type: f1
value: 0.6978625703821133
---
<!-- 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. -->
# rubert-base-cased-finetuned-panx-de
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3352
- F1: 0.6979
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3039 | 1.0 | 131 | 1.0720 | 0.5871 |
| 0.7911 | 2.0 | 262 | 0.9581 | 0.6335 |
| 0.5448 | 3.0 | 393 | 0.9162 | 0.6718 |
| 0.3709 | 4.0 | 524 | 0.9722 | 0.6819 |
| 0.2565 | 5.0 | 655 | 1.0484 | 0.6847 |
| 0.1923 | 6.0 | 786 | 1.1410 | 0.6924 |
| 0.145 | 7.0 | 917 | 1.1692 | 0.6995 |
| 0.1104 | 8.0 | 1048 | 1.2592 | 0.6981 |
| 0.086 | 9.0 | 1179 | 1.3307 | 0.6972 |
| 0.0715 | 10.0 | 1310 | 1.3352 | 0.6979 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
JosephusCheung/ACertainModel | JosephusCheung | 2022-12-20T03:16:49Z | 274 | 160 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"arxiv:2106.09685",
"doi:10.57967/hf/0196",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-12T17:40:00Z | ---
language:
- en
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
widget:
- text: "masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden"
example_title: "example 1girl"
- text: "masterpiece, best quality, 1boy, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden"
example_title: "example 1boy"
---
# ACertainModel
**Try full functions with Google Colab free T4** [](https://colab.research.google.com/drive/1ldhBc70wvuvkp4Af_vNTzTfBXwpf_cH5?usp=sharing)
Check Twitter [#ACertainModel](https://twitter.com/hashtag/ACertainModel) for community artworks
Welcome to ACertainModel - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style pictures with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags, including artists, to generate images.
Since I noticed that the laion-aesthetics introduced in the Stable-Diffusion-v-1-4 checkpoint hindered finetuning anime style illustration generation model, Dreambooth was used to finetune some tags separately to make it closer to what it was in SD1.2. To avoid overfitting and possible language drift, I added a huge amount of auto-generated pictures from a single word prompt to the training set, using models that are popular in the community such as Anything-3.0, together with partially manual selected full-danbooru images within a year, for further native training. I am also aware of a method of [LoRA](https://arxiv.org/abs/2106.09685), with a similar idea, finetuning attention layer solely, to have better performance on eyes, hands, and other details.
For copyright compliance and technical experiment, it was trained from few artist images directly. It was trained on Dreambooth with pictures generated from several popular diffusion models in the community. The checkpoint was initialized with the weights of a Stable Diffusion Model and subsequently fine-tuned for 2K GPU hours on V100 32GB and 600 GPU hours on A100 40GB at 512P dynamic aspect ratio resolution with a certain ratio of unsupervised auto-generated images from several popular diffusion models in the community with some Textual Inversions and Hypernetworks. We do know some tricks on xformers and 8-bit optimization, but we didn't use any of them for better quality and stability. Up to 15 branches are trained simultaneously, cherry-picking about every 20,000 steps.
e.g. **_masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden_**
## About online preview with Hosted inference API, also generation with this model
Parameters are not allowed to be modified, as it seems that it is generated with *Clip skip: 1*, for better performance, it is strongly recommended to use *Clip skip: 2* instead.
Here is an example of inference settings, if it is applicable with you on your own server: *Steps: 28, Sampler: Euler a, CFG scale: 11, Clip skip: 2*.
## 𧨠Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or FLAX/JAX.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "JosephusCheung/ACertainModel"
branch_name= "main"
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision=branch_name, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "pikachu"
image = pipe(prompt).images[0]
image.save("./pikachu.png")
```
## Examples
Below are some examples of images generated using this model, with better performance on framing and hand gestures, as well as moving objects, comparing to other analogues:
**Anime Girl:**

```
1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2
```
**Anime Boy:**

```
1boy, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2
```
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## Is it a NovelAI based model? What is the relationship with SD1.2 and SD1.4?
See [ASimilarityCalculatior](https://huggingface.co/JosephusCheung/ASimilarityCalculatior) |
sryu1/dqn-SpaceInvadersNoFrameskip-v4 | sryu1 | 2022-12-20T03:14:10Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T03:13:37Z | ---
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: 520.50 +/- 180.17
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 sryu1 -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 sryu1 -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 sryu1
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Antiraedus/Taxiv3-base | Antiraedus | 2022-12-20T03:12:46Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T03:12:40Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxiv3-base
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="Antiraedus/Taxiv3-base", 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"])
```
|
Antiraedus/q-FrozenLake-v1-4x4-noSlippery | Antiraedus | 2022-12-20T03:05:45Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T03:05:40Z | ---
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="Antiraedus/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"])
```
|
EatingKing/testmodel | EatingKing | 2022-12-20T03:05:42Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-01T07:16:56Z | - π Hi, Iβm @EatingKing001
- π Iβm interested in ...
- π± Iβm currently learning ...
- ποΈ Iβm looking to collaborate on ...
- π« How to reach me ...
<!---
EatingKing001/EatingKing001 is a β¨ special β¨ repository because its `README.md` (this file) appears on your GitHub profile.
You can click the Preview link to take a look at your changes.
--->
|
jinghua2tang/dqn-SpaceInvadersNoFrameskip-v4 | jinghua2tang | 2022-12-20T03:02:41Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T03:01:57Z | ---
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: 670.00 +/- 198.42
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 jinghua2tang -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 jinghua2tang -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 jinghua2tang
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
aki6022/finetuning-sentiment-model-3000-samples-practice | aki6022 | 2022-12-20T02:29:28Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-20T02:07:03Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: finetuning-sentiment-model-3000-samples-practice
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples-practice
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb 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: 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 2.8.0
- Tokenizers 0.13.2
|
Bluelemon883/ppo-LunarLander-v2 | Bluelemon883 | 2022-12-20T01:32:21Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T01:31:55Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.28 +/- 24.84
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
...
```
|
yanick/ppo-Huggy | yanick | 2022-12-20T01:26:13Z | 19 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2022-12-20T01:26:00Z |
---
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: yanick/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
MichaelCHomeX/rare-puppers | MichaelCHomeX | 2022-12-20T01:23:21Z | 20 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-12-20T01:23:00Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.939393937587738
---
# rare-puppers
Autogenerated by HuggingPicsπ€πΌοΈ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi

#### samoyed

#### shiba inu
 |
trulymadlydeeply/Chinese-birds-flying-1 | trulymadlydeeply | 2022-12-20T00:42:09Z | 86 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-12-20T00:41:44Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Chinese-birds-flying-1
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8072289228439331
---
# Chinese-birds-flying-1
Autogenerated by HuggingPicsπ€πΌοΈ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### egret flying

#### paradise flycatcher flying

#### red-crowned crane flying

#### sparrowhawk flying

#### swan flying
 |
keithrebello/ppo-LunarLander-v2 | keithrebello | 2022-12-20T00:39:52Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T00:39:27Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.55 +/- 20.10
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
...
```
|
jpequegn/ppo-LunarLander-v2 | jpequegn | 2022-12-20T00:32:33Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-20T00:31:57Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.47 +/- 17.56
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
...
```
|
elRivx/reasonableDrinkV2 | elRivx | 2022-12-20T00:12:12Z | 0 | 1 | null | [
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2022-12-19T23:51:25Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
# reasonableDrinkV2
This is an update of my own SD trainee with a many lot of dreaming (or nightmare) illustrations as a style to SD 2.1.
If you wanna test it, you can put this word on the prompt: reasonableDrink
If you enjoy my work, please consider supporting me:
[](https://www.buymeacoffee.com/elrivx)
Examples:
<img src=https://imgur.com/ltYgB5B.png width=30% height=30%>
<img src=https://imgur.com/a3w0ltf.png width=30% height=30%>
<img src=https://imgur.com/JRi6wwT.png width=30% height=30%>
<img src=https://imgur.com/QWe03qH.png width=30% height=30%>
<img src=https://imgur.com/m2Dz20C.png width=30% height=30%>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
hku-nlp/instructor-base | hku-nlp | 2022-12-19T23:40:12Z | 196 | 6 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-12-17T20:01:46Z | ---
pipeline_tag: sentence-similarity
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# hku-nlp/instructor-base
This is a general embedding model: It maps **any** piece of text (e.g., a title, a sentence, a document, etc.) to a fixed-length vector in test time **without further training**. With instructions, the embeddings are **domain-specific** (e.g., specialized for science, finance, etc.) and **task-aware** (e.g., customized for classification, information retrieval, etc.)
The model is easy to use with `sentence-transformer` library.
## Installation
```bash
git clone https://github.com/HKUNLP/instructor-embedding
cd sentence-transformers
pip install -e .
```
## Compute your customized embeddings
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
```python
from sentence_transformers import SentenceTransformer
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title; Input:"
model = SentenceTransformer('hku-nlp/instructor-base')
embeddings = model.encode([[instruction,sentence,0]])
print(embeddings)
```
## Calculate Sentence similarities
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
```python
from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0],
['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]
sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0],
['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)
``` |
FabioQuintao/q-FrozenLake-v1-4x4-noSlippery | FabioQuintao | 2022-12-19T23:00:47Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T23:00:38Z | ---
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 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="FabioQuintao/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"])
```
|
DrishtiSharma/whisper-large-v2-serbian-400-steps | DrishtiSharma | 2022-12-19T22:59:24Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"sr",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T22:27:12Z | ---
language:
- sr
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 Serbian - 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: sr
split: test
args: sr
metrics:
- name: Wer
type: wer
value: 11.007689194658035
---
<!-- 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 Serbian - Drishti Sharma
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: 0.2142
- Wer: 11.0077
## 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: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0965 | 1.91 | 400 | 0.2142 | 11.0077 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
anuragshas/whisper-large-v2-mt | anuragshas | 2022-12-19T22:24:18Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"mt",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T19:49:23Z | ---
language:
- mt
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 Maltese
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 mt
type: mozilla-foundation/common_voice_11_0
config: mt
split: test
args: mt
metrics:
- name: Wer
type: wer
value: 18.464443960194668
---
<!-- 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 Maltese
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 mt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3616
- Wer: 18.4644
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0023 | 9.0 | 1000 | 0.3616 | 18.4644 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
sabamedia/saba | sabamedia | 2022-12-19T22:14:39Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
]
| null | 2022-12-19T22:11:08Z | ---
license: afl-3.0
---
bird flying high on a dark place with big owl eyes on background |
huggingtweets/louisetatmaia | huggingtweets | 2022-12-19T21:52:24Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-12-19T21:51:38Z | ---
language: en
thumbnail: http://www.huggingtweets.com/louisetatmaia/1671486739770/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1592135191883124736/KESXJNh2_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ AI BOT π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">louiseππ―β</div>
<div style="text-align: center; font-size: 14px;">@louisetatmaia</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.

To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from louiseππ―β.
| Data | louiseππ―β |
| --- | --- |
| Tweets downloaded | 3187 |
| Retweets | 1259 |
| Short tweets | 328 |
| Tweets kept | 1600 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3bubvh06/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @louisetatmaia's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2iq7510b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2iq7510b/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/louisetatmaia')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Orokusaki/ppo-LunarLander-v2 | Orokusaki | 2022-12-19T21:41:01Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T21:40:23Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.37 +/- 23.97
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
IngoTB303/q-FrozenLake-v1-4x4-noSlippery | IngoTB303 | 2022-12-19T21:24:55Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T21:24:42Z | ---
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="IngoTB303/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"])
```
|
SamoaJon/q-Taxi-v3 | SamoaJon | 2022-12-19T21:24:52Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T21:24:35Z | ---
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="SamoaJon/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"])
```
|
shripadbhat/whisper-large-v2-lt | shripadbhat | 2022-12-19T21:19:12Z | 9 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"lt",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T18:09:13Z | ---
language:
- lt
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 Lithuanian
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: lt
split: test
args: lt
metrics:
- name: Wer
type: wer
value: 29.932107833406125
---
<!-- 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 Lithuanian
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: 0.3421
- Wer: 29.9321
## 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: 50
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4255 | 0.09 | 100 | 0.4323 | 37.0310 |
| 0.2976 | 0.18 | 200 | 0.3421 | 29.9321 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
benhamilto/test-model | benhamilto | 2022-12-19T21:00:15Z | 0 | 0 | diffusers | [
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2022-12-15T13:58:50Z | Tester
---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
|
utyug1/q-Taxi-v3 | utyug1 | 2022-12-19T20:58:14Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T20:58:08Z | ---
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: 7.38 +/- 2.84
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="utyug1/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"])
```
|
SamoaJon/q-FrozenLake-v1-4x4-noSlippery | SamoaJon | 2022-12-19T20:58:04Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"doi:10.57967/hf/0220",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-15T14:46:01Z | ---
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="SamoaJon/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"])
```
|
Arch4ngel/dqn-SpaceInvadersNoFrameskip-v4 | Arch4ngel | 2022-12-19T20:49:20Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T20:48:43Z | ---
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: 580.50 +/- 170.08
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 Arch4ngel -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 Arch4ngel -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 Arch4ngel
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
bsmith0430/q-FrozenLake-v1-4x4-noSlippery | bsmith0430 | 2022-12-19T20:29:02Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-11-29T01:03:05Z | ---
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="bsmith0430/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"])
```
|
apugachev/roberta-large-boolq-finetuned | apugachev | 2022-12-19T20:13:13Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T20:05:11Z | Training parameters:
```
model_args = ClassificationArgs()
model_args.max_seq_length = 512
model_args.train_batch_size = 12
model_args.eval_batch_size = 12
model_args.num_train_epochs = 5
model_args.evaluate_during_training = False
model_args.learning_rate = 1e-5
model_args.use_multiprocessing = False
model_args.fp16 = False
model_args.save_steps = -1
model_args.save_eval_checkpoints = False
model_args.no_cache = True
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
```
Evaluation on BoolQ Test Set:
| | Precision | Recall | F1-score |
|:------------:|:---------:|:------:|:--------:|
| 0 | 0.82 | 0.80 | 0.81 |
| 1 | 0.88 | 0.89 | 0.88 |
| accuracy | | | 0.86 |
| macro avg | 0.85 | 0.84 | 0.85 |
| weighted avg | 0.86 | 0.86 | 0.86 |
ROC AUC Score: 0.844 |
kpriyanshu256/whisper-large-v2-br-1000-32-1e-05 | kpriyanshu256 | 2022-12-19T20:06:18Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"br",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T05:02:19Z | ---
language:
- br
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-large-v2-breton
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: br
split: test
args: br
metrics:
- name: Wer
type: wer
value: 39.92705800625217
---
<!-- 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-large-v2-breton
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: 0.7162
- Wer: 39.9271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- 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: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7423 | 0.1 | 100 | 0.8363 | 57.1553 |
| 0.4361 | 1.07 | 200 | 0.6833 | 46.7176 |
| 0.2227 | 2.03 | 300 | 0.6483 | 42.5929 |
| 0.1472 | 3.0 | 400 | 0.6511 | 42.4627 |
| 0.0892 | 3.1 | 500 | 0.6633 | 40.9604 |
| 0.0651 | 4.07 | 600 | 0.6807 | 39.7534 |
| 0.0416 | 5.04 | 700 | 0.6870 | 41.2383 |
| 0.0352 | 6.0 | 800 | 0.7315 | 39.9010 |
| 0.022 | 6.1 | 900 | 0.7201 | 40.4307 |
| 0.0195 | 7.07 | 1000 | 0.7162 | 39.9271 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
rama100/q-Taxi-v | rama100 | 2022-12-19T19:56:13Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T19:56:10Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v
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="rama100/q-Taxi-v", 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"])
```
|
NeelK94/Taxi-v3 | NeelK94 | 2022-12-19T19:38:20Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T19:38:12Z | ---
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="NeelK94/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"])
```
|
togoforfood/ppo-LunarLander-v2 | togoforfood | 2022-12-19T19:30:01Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T19:29:37Z | ---
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: 263.05 +/- 24.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ahmadmwali/finetuning-sentiment-igbo21 | ahmadmwali | 2022-12-19T19:13:14Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T18:16:25Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-igbo21
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-igbo21
This model is a fine-tuned version of [mbeukman/xlm-roberta-base-finetuned-ner-igbo](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-igbo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5368
- Accuracy: 0.7923
- F1: 0.7914
## 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 2.8.0
- Tokenizers 0.13.2
|
Rami/CartPole-v1__functional_dqn__0__1671476597 | Rami | 2022-12-19T19:07:38Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-19T19:07:27Z | ---
language: en
license: apache-2.0
model-index:
- name: CartPole-v1__functional_dqn__0__1671476597
---
DQN model applied to the this discrete environments CartPole-v1
## Model Description
The model was trained from the CleanRl library using the DQN algorithm
## Intended Use & Limitation
The model is intended to be used for the following environments CartPole-v1
and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure
### Training Hyperparameters
```
The folloing hyperparameters were used during training:
- exp_name: functional_dqn
- seed: 0
- torch_deterministic: True
- cuda: False
- track: True
- wandb_project_name: cleanRL
- wandb_entity: compress_rl
- capture_video: False
- env_id: CartPole-v1
- total_timesteps: 500000
- learning_rate: 0.00025
- buffer_size: 10000
- gamma: 0.99
- target_network_frequency: 500
- batch_size: 128
- start_e: 1
- end_e: 0.05
- exploration_fraction: 0.5
- learning_starts: 10000
- train_frequency: 10
- optimizer: Adan
- wandb_project: cleanrl
```
### Framework and version
```
Pytorch 1.12.1+cu102
gym 0.23.1
Weights and Biases 0.13.3
Hugging Face Hub 0.11.1
|
Rami/CartPole-v1__functional_dqn__0__1671475909 | Rami | 2022-12-19T18:55:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-19T18:55:48Z | ---
language: en
license: apache-2.0
model-index:
- name: CartPole-v1__functional_dqn__0__1671475909
---
DQN model applied to the this discrete environments CartPole-v1
## Model Description
The model was trained from the CleanRl library using the DQN algorithm
## Intended Use & Limitation
The model is intended to be used for the following environments CartPole-v1
and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure
### Training Hyperparameters
```
The folloing hyperparameters were used during training:
- exp_name: functional_dqn
- seed: 0
- torch_deterministic: True
- cuda: False
- track: True
- wandb_project_name: cleanRL
- wandb_entity: compress_rl
- capture_video: False
- env_id: CartPole-v1
- total_timesteps: 500000
- learning_rate: 0.00025
- buffer_size: 10000
- gamma: 0.99
- target_network_frequency: 500
- batch_size: 128
- start_e: 1
- end_e: 0.05
- exploration_fraction: 0.5
- learning_starts: 10000
- train_frequency: 10
- optimizer: Adan
- wandb_project: cleanrl
```
### Framework and version
```
Pytorch 1.12.1+cu102
gym 0.23.1
Weights and Biases 0.13.3
Hugging Face Hub 0.11.1
|
Roberto/Taxi-v3 | Roberto | 2022-12-19T18:54:28Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-12-19T17:12:31Z | ---
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="Roberto/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"])
```
|
hyorea1/KoT5-test-add-data-prefix-summary | hyorea1 | 2022-12-19T18:43:35Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-12-19T09:57:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: KoT5-test-add-data-prefix-summary
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. -->
# KoT5-test-add-data-prefix-summary
This model is a fine-tuned version of [hyorea1/KoT5-test-add-data-prefix-summary](https://huggingface.co/hyorea1/KoT5-test-add-data-prefix-summary) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1781
- Rouge1: 11.8533
- Rouge2: 2.9172
- Rougel: 11.715
- Rougelsum: 11.7278
- Gen Len: 35.164
## 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: 4
- eval_batch_size: 4
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.4974 | 0.32 | 800 | 1.1935 | 11.0529 | 3.0383 | 10.9308 | 10.9481 | 34.8809 |
| 1.0394 | 0.64 | 1600 | 1.1979 | 11.2828 | 2.8757 | 11.1691 | 11.1952 | 35.6412 |
| 1.2385 | 0.97 | 2400 | 1.1914 | 10.8007 | 3.0248 | 10.696 | 10.7022 | 34.8081 |
| 1.4298 | 1.29 | 3200 | 1.1916 | 10.8949 | 2.9547 | 10.8037 | 10.832 | 34.7934 |
| 1.3735 | 1.61 | 4000 | 1.1887 | 11.8127 | 3.2642 | 11.7143 | 11.7263 | 35.4331 |
| 1.5772 | 1.93 | 4800 | 1.1794 | 11.3157 | 3.1017 | 11.2215 | 11.2237 | 34.3051 |
| 1.2179 | 2.25 | 5600 | 1.1809 | 11.841 | 2.8297 | 11.7283 | 11.7173 | 35.0522 |
| 1.2903 | 2.58 | 6400 | 1.1779 | 11.6353 | 2.8495 | 11.5117 | 11.544 | 34.95 |
| 1.461 | 2.9 | 7200 | 1.1781 | 11.8533 | 2.9172 | 11.715 | 11.7278 | 35.164 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
Rami/CartPole-v1__functional_dqn__0__1671474891 | Rami | 2022-12-19T18:39:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-12-19T18:38:52Z | ---
language: en
license: apache-2.0
model-index:
- name: CartPole-v1__functional_dqn__0__1671474891
---
DQN model applied to the this discrete environments CartPole-v1
## Model Description
The model was trained from the CleanRl library using the DQN algorithm
## Intended Use & Limitation
The model is intended to be used for the following environments CartPole-v1
and understand the implication of Quantization on this type of model from a pretrained state## Training Procdure
### Training Hyperparameters
```
The folloing hyperparameters were used during training:
- exp_name: functional_dqn
- seed: 0
- torch_deterministic: True
- cuda: False
- track: True
- wandb_project_name: cleanRL
- wandb_entity: compress_rl
- capture_video: False
- env_id: CartPole-v1
- total_timesteps: 500000
- learning_rate: 0.00025
- buffer_size: 10000
- gamma: 0.99
- target_network_frequency: 500
- batch_size: 128
- start_e: 1
- end_e: 0.05
- exploration_fraction: 0.5
- learning_starts: 10000
- train_frequency: 10
- optimizer: Adam
- wandb_project: cleanrl
```
### Framework and version
```
Pytorch 1.12.1+cu102
gym 0.23.1
Weights and Biases 0.13.3
Hugging Face Hub 0.11.1
|
Martha-987/whisper-small-ArabicAr | Martha-987 | 2022-12-19T18:20:58Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ar",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-16T14:10:52Z | ---
language:
- ar
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Ar- Martha
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: ar
split: test
metrics:
- name: Wer
type: wer
value: 50.11110090900743
---
<!-- 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 Ar- Martha
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: 0.3743
- Wer: 50.1111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.26 | 0.42 | 1000 | 0.3743 | 50.1111 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
kevinbror/distilbert-base-uncased-finetuned-cola | kevinbror | 2022-12-19T18:10:13Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-12-19T17:59:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.22961097671530944
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5451
- Matthews Correlation: 0.2296
## 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: 0.5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| No log | 0.5 | 268 | 0.5451 | 0.2296 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Jingmiao/whisper-small-chinese_base | Jingmiao | 2022-12-19T18:00:53Z | 262 | 22 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-17T17:06:26Z | ---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Chinese Base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs cmn_hans_cn
type: google/fleurs
config: cmn_hans_cn
split: test
args: cmn_hans_cn
metrics:
- name: Wer
type: wer
value: 16.643891773708663
---
<!-- 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 Chinese Base
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs cmn_hans_cn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3573
- Wer: 16.6439
## 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0005 | 76.0 | 1000 | 0.3573 | 16.6439 |
| 0.0002 | 153.0 | 2000 | 0.3897 | 16.9749 |
| 0.0001 | 230.0 | 3000 | 0.4125 | 17.2330 |
| 0.0001 | 307.0 | 4000 | 0.4256 | 17.2451 |
| 0.0001 | 384.0 | 5000 | 0.4330 | 17.2300 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
emilios/whisper-md-hr | emilios | 2022-12-19T17:58:16Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"dataset:google/fleurs",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-12-19T11:45:57Z | ---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper medium Croatian El Greco
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs hr_hr
type: google/fleurs
config: zu
split: None
metrics:
- name: Wer
type: wer
value: 14.613261224719734
---
<!-- 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 Croatian El Greco
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs hr_hr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3374
- Wer: 14.6133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0106 | 4.61 | 1000 | 0.3374 | 14.6133 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221216+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
Subsets and Splits