modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 18:27:28
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 532
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-01 18:27:19
| card
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|---|---|---|---|---|---|---|---|---|---|
Shaier/medqa_fine_tuned_linkbert
|
Shaier
| 2022-07-12T04:48:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-07-12T03:27:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: medqa_fine_tuned
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. -->
# medqa_fine_tuned
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4462
- Accuracy: 0.4002
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.3208 | 0.3553 |
| 1.2802 | 2.0 | 636 | 1.3428 | 0.3703 |
| 1.2802 | 3.0 | 954 | 1.3780 | 0.3892 |
| 1.1466 | 4.0 | 1272 | 1.4234 | 0.3978 |
| 1.052 | 5.0 | 1590 | 1.4462 | 0.4002 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.11.0
|
Saraswati/q-FrozenLake-v1-4x4-noSlippery
|
Saraswati
| 2022-07-12T04:25:49Z | 0 | 1 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-12T04:25:40Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Saraswati/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Evelyn18/legalectra-small-spanish-becasv3-2
|
Evelyn18
| 2022-07-12T04:24:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-12T04:00:10Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-2
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7145
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.7994 |
| No log | 2.0 | 10 | 5.6445 |
| No log | 3.0 | 15 | 5.5595 |
| No log | 4.0 | 20 | 5.4933 |
| No log | 5.0 | 25 | 5.4248 |
| No log | 6.0 | 30 | 5.3547 |
| No log | 7.0 | 35 | 5.2872 |
| No log | 8.0 | 40 | 5.2187 |
| No log | 9.0 | 45 | 5.1585 |
| No log | 10.0 | 50 | 5.1038 |
| No log | 11.0 | 55 | 5.0451 |
| No log | 12.0 | 60 | 5.0015 |
| No log | 13.0 | 65 | 4.9638 |
| No log | 14.0 | 70 | 4.9350 |
| No log | 15.0 | 75 | 4.9034 |
| No log | 16.0 | 80 | 4.8741 |
| No log | 17.0 | 85 | 4.8496 |
| No log | 18.0 | 90 | 4.8275 |
| No log | 19.0 | 95 | 4.8139 |
| No log | 20.0 | 100 | 4.7878 |
| No log | 21.0 | 105 | 4.7672 |
| No log | 22.0 | 110 | 4.7671 |
| No log | 23.0 | 115 | 4.7611 |
| No log | 24.0 | 120 | 4.7412 |
| No log | 25.0 | 125 | 4.7307 |
| No log | 26.0 | 130 | 4.7232 |
| No log | 27.0 | 135 | 4.7208 |
| No log | 28.0 | 140 | 4.7186 |
| No log | 29.0 | 145 | 4.7158 |
| No log | 30.0 | 150 | 4.7145 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/legalectra-small-spanish-becasv3-1
|
Evelyn18
| 2022-07-12T03:54:49Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-12T03:49:49Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-1
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5694
## 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: 10
- eval_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 8 | 5.8980 |
| No log | 2.0 | 16 | 5.8136 |
| No log | 3.0 | 24 | 5.7452 |
| No log | 4.0 | 32 | 5.6940 |
| No log | 5.0 | 40 | 5.6554 |
| No log | 6.0 | 48 | 5.6241 |
| No log | 7.0 | 56 | 5.5997 |
| No log | 8.0 | 64 | 5.5830 |
| No log | 9.0 | 72 | 5.5730 |
| No log | 10.0 | 80 | 5.5694 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
paola-md/recipe-distilbert-upper-Is
|
paola-md
| 2022-07-12T03:03:14Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-12T00:16:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-distilbert-upper-Is
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. -->
# recipe-distilbert-upper-Is
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8565
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6309 | 1.0 | 1305 | 1.2607 |
| 1.2639 | 2.0 | 2610 | 1.1291 |
| 1.1592 | 3.0 | 3915 | 1.0605 |
| 1.0987 | 4.0 | 5220 | 1.0128 |
| 1.0569 | 5.0 | 6525 | 0.9796 |
| 1.0262 | 6.0 | 7830 | 0.9592 |
| 1.0032 | 7.0 | 9135 | 0.9352 |
| 0.9815 | 8.0 | 10440 | 0.9186 |
| 0.967 | 9.0 | 11745 | 0.9086 |
| 0.9532 | 10.0 | 13050 | 0.8973 |
| 0.9436 | 11.0 | 14355 | 0.8888 |
| 0.9318 | 12.0 | 15660 | 0.8835 |
| 0.9243 | 13.0 | 16965 | 0.8748 |
| 0.9169 | 14.0 | 18270 | 0.8673 |
| 0.9117 | 15.0 | 19575 | 0.8610 |
| 0.9066 | 16.0 | 20880 | 0.8562 |
| 0.9028 | 17.0 | 22185 | 0.8566 |
| 0.901 | 18.0 | 23490 | 0.8583 |
| 0.8988 | 19.0 | 24795 | 0.8557 |
| 0.8958 | 20.0 | 26100 | 0.8565 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nateraw/yolov6t
|
nateraw
| 2022-07-12T02:01:04Z | 0 | 0 |
pytorch
|
[
"pytorch",
"object-detection",
"yolo",
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:gpl-3.0",
"region:us"
] |
object-detection
| 2022-07-08T04:19:38Z |
---
language: en
license: gpl-3.0
library_name: pytorch
tags:
- object-detection
- yolo
- autogenerated-modelcard
model_name: yolov6t
---
# Model Card for yolov6t
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Related Models:** [yolov6s](https://hf.co/nateraw/yolov6s), [yolov6n](https://hf.co/nateraw/yolov6n)
- **Parent Model:** N/A
- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is meant to be used as a general object detector.
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
You can fine-tune this model for your specific task
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Don't be evil.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
# Model Examination
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Model Card Authors [optional]
[@nateraw](https://hf.co/nateraw)
# Model Card Contact
[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details>
|
ArthurBaia/xlm-roberta-base-squad-pt
|
ArthurBaia
| 2022-07-11T22:42:37Z | 7 | 2 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v1_pt",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-11T16:59:16Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_v1_pt
model-index:
- name: xlm-roberta-base-squad-pt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-squad-pt
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad_v1_pt dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
- "epoch": 3.0,
- "eval_exact_match": 44.45600756859035,
- "eval_f1": 57.37953911779836,
- "eval_samples": 11095
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ManqingLiu/pegasus-samsum
|
ManqingLiu
| 2022-07-11T22:33:51Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-11T21:16:06Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7236 | 0.54 | 500 | 1.4858 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.10.3
|
mariastull/testpyramidsrnd
|
mariastull
| 2022-07-11T22:28:45Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-07-11T22:28:40Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: mariastull/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
tj-solergibert/distilbert-base-uncased-finetuned-emotion
|
tj-solergibert
| 2022-07-11T21:58:32Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-11T17:19:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.9285646975197546
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2158
- Accuracy: 0.9285
- F1: 0.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8235 | 1.0 | 250 | 0.3085 | 0.915 | 0.9127 |
| 0.2493 | 2.0 | 500 | 0.2158 | 0.9285 | 0.9286 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
camilag/t5-end2end-questions-generation
|
camilag
| 2022-07-11T20:52:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad_modified_for_t5_qg",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-11T20:12:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_modified_for_t5_qg
model-index:
- name: t5-end2end-questions-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad_modified_for_t5_qg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5425 | 0.34 | 100 | 1.9416 |
| 2.0221 | 0.68 | 200 | 1.7927 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_vp-it_s738
|
jonatasgrosman
| 2022-07-11T20:09:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T20:08:31Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-it_s738
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-it_s996
|
jonatasgrosman
| 2022-07-11T19:59:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:58:21Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-it_s996
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
paola-md/recipe-roberta-tis
|
paola-md
| 2022-07-11T19:45:57Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-11T16:22:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: recipe-roberta-tis
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. -->
# recipe-roberta-tis
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8491
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3552 | 1.0 | 1012 | 1.1292 |
| 1.1811 | 2.0 | 2024 | 1.0543 |
| 1.1095 | 3.0 | 3036 | 1.0122 |
| 1.0667 | 4.0 | 4048 | 0.9756 |
| 1.0345 | 5.0 | 5060 | 0.9478 |
| 1.0112 | 6.0 | 6072 | 0.9292 |
| 0.9922 | 7.0 | 7084 | 0.9137 |
| 0.9762 | 8.0 | 8096 | 0.9056 |
| 0.9627 | 9.0 | 9108 | 0.8977 |
| 0.9507 | 10.0 | 10120 | 0.8868 |
| 0.9411 | 11.0 | 11132 | 0.8823 |
| 0.9344 | 12.0 | 12144 | 0.8745 |
| 0.9261 | 13.0 | 13156 | 0.8688 |
| 0.9189 | 14.0 | 14168 | 0.8614 |
| 0.9133 | 15.0 | 15180 | 0.8609 |
| 0.9078 | 16.0 | 16192 | 0.8581 |
| 0.906 | 17.0 | 17204 | 0.8544 |
| 0.9015 | 18.0 | 18216 | 0.8537 |
| 0.8988 | 19.0 | 19228 | 0.8494 |
| 0.8975 | 20.0 | 20240 | 0.8491 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_xls-r_s657
|
jonatasgrosman
| 2022-07-11T19:45:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:44:32Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xls-r_s657
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
KD02/distilbert-base-uncased-finetuned-squad
|
KD02
| 2022-07-11T19:37:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-11T14:14:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [KD02/distilbert-base-uncased-finetuned-squad](https://huggingface.co/KD02/distilbert-base-uncased-finetuned-squad) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Sahara/finetuning-sentiment-model-3000-samples
|
Sahara
| 2022-07-11T19:23:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-11T14:06:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8533333333333334
- name: F1
type: f1
value: 0.8562091503267975
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3322
- Accuracy: 0.8533
- F1: 0.8562
## 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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_vp-nl_s6
|
jonatasgrosman
| 2022-07-11T19:17:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:16:53Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-nl_s6
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-nl_s833
|
jonatasgrosman
| 2022-07-11T19:13:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:12:53Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-nl_s833
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-es_s291
|
jonatasgrosman
| 2022-07-11T19:09:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T19:08:58Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-es_s291
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-fr_s752
|
jonatasgrosman
| 2022-07-11T18:58:10Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:57:25Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s752
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-fr_s485
|
jonatasgrosman
| 2022-07-11T18:54:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:53:30Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s485
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-fr_s675
|
jonatasgrosman
| 2022-07-11T18:49:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:48:25Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s675
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
quanxi/dqn-SpaceInvadersNoFrameskip-v4
|
quanxi
| 2022-07-11T18:32:52Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T18:32:11Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 596.50 +/- 113.18
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **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 utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga quanxi -f logs/
python enjoy.py --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 utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga quanxi
```
## 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', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
jonatasgrosman/exp_w2v2t_pt_unispeech-ml_s808
|
jonatasgrosman
| 2022-07-11T18:31:15Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:30:46Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s808
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_unispeech-ml_s324
|
jonatasgrosman
| 2022-07-11T18:27:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:26:59Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s324
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wavlm_s691
|
jonatasgrosman
| 2022-07-11T18:13:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:13:02Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wavlm_s691
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wavlm_s51
|
jonatasgrosman
| 2022-07-11T18:10:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:09:52Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wavlm_s51
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_no-pretraining_s34
|
jonatasgrosman
| 2022-07-11T18:06:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T18:05:36Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_no-pretraining_s34
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-sv_s563
|
jonatasgrosman
| 2022-07-11T17:51:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:50:36Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s563
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdiKompella/Reinforce-Pixelcopter-PLE-v0
|
AdiKompella
| 2022-07-11T17:48:01Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T17:47:44Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- metrics:
- type: mean_reward
value: 12.70 +/- 11.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
jonatasgrosman/exp_w2v2t_pt_hubert_s486
|
jonatasgrosman
| 2022-07-11T17:43:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:42:50Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s486
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_hubert_s301
|
jonatasgrosman
| 2022-07-11T17:40:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:39:41Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s301
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_hubert_s807
|
jonatasgrosman
| 2022-07-11T17:36:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:36:06Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s807
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ianspektor/reinforce-CartPole-v1
|
ianspektor
| 2022-07-11T17:36:19Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T16:33:35Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-CartPole-v1
results:
- metrics:
- type: mean_reward
value: 359.42 +/- 89.49
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
kinanmartin/xlm-roberta-large-ner-hrl-finetuned-ner
|
kinanmartin
| 2022-07-11T17:29:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:toydata",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-11T03:49:46Z |
---
tags:
- generated_from_trainer
datasets:
- toydata
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-ner-hrl-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: toydata
type: toydata
args: SDN
metrics:
- name: Precision
type: precision
value: 0.9132452695465905
- name: Recall
type: recall
value: 0.9205854126679462
- name: F1
type: f1
value: 0.9169006511739053
- name: Accuracy
type: accuracy
value: 0.9784804945824268
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-ner-hrl-finetuned-ner
This model is a fine-tuned version of [Davlan/xlm-roberta-large-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-large-ner-hrl) on the toydata dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0944
- Precision: 0.9132
- Recall: 0.9206
- F1: 0.9169
- Accuracy: 0.9785
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 408 | 0.0900 | 0.8508 | 0.9303 | 0.8888 | 0.9719 |
| 0.1087 | 2.0 | 816 | 0.0827 | 0.9043 | 0.9230 | 0.9136 | 0.9783 |
| 0.0503 | 3.0 | 1224 | 0.0944 | 0.9132 | 0.9206 | 0.9169 | 0.9785 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_xlsr-53_s829
|
jonatasgrosman
| 2022-07-11T17:23:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:23:00Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xlsr-53_s829
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_xlsr-53_s677
|
jonatasgrosman
| 2022-07-11T17:17:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T17:16:33Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xlsr-53_s677
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wav2vec2_s859
|
jonatasgrosman
| 2022-07-11T16:58:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T16:57:41Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wav2vec2_s859
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wav2vec2_s250
|
jonatasgrosman
| 2022-07-11T16:51:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T16:51:14Z |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wav2vec2_s250
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_vp-it_s179
|
jonatasgrosman
| 2022-07-11T16:44:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T16:44:09Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-it_s179
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jorge-henao/gpt2-small-spanish-historias-conflicto-colpoetry-historias-conflicto-col
|
jorge-henao
| 2022-07-11T16:43:58Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-11T16:29:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-small-spanish-historias-conflicto-colpoetry-historias-conflicto-col
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-small-spanish-historias-conflicto-colpoetry-historias-conflicto-col
This model is a fine-tuned version of [jorge-henao/gpt2-small-spanish-historias-conflicto-col](https://huggingface.co/jorge-henao/gpt2-small-spanish-historias-conflicto-col) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5017
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
alefarasin/testpyramidsrnd
|
alefarasin
| 2022-07-11T16:37:44Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-07-11T16:37:35Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: alefarasin/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
jonatasgrosman/exp_w2v2t_es_r-wav2vec2_s809
|
jonatasgrosman
| 2022-07-11T16:26:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T16:26:08Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_r-wav2vec2_s809
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
paola-md/recipe-roberta-i
|
paola-md
| 2022-07-11T16:17:54Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-11T15:48:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: recipe-roberta-i
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. -->
# recipe-roberta-i
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9919
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.871 | 1.0 | 149 | 1.4670 |
| 1.528 | 2.0 | 298 | 1.3426 |
| 1.41 | 3.0 | 447 | 1.2636 |
| 1.3332 | 4.0 | 596 | 1.2029 |
| 1.2804 | 5.0 | 745 | 1.1646 |
| 1.2441 | 6.0 | 894 | 1.1351 |
| 1.21 | 7.0 | 1043 | 1.0898 |
| 1.182 | 8.0 | 1192 | 1.0725 |
| 1.1604 | 9.0 | 1341 | 1.0718 |
| 1.1402 | 10.0 | 1490 | 1.0529 |
| 1.1308 | 11.0 | 1639 | 1.0512 |
| 1.1191 | 12.0 | 1788 | 1.0245 |
| 1.0986 | 13.0 | 1937 | 1.0203 |
| 1.0919 | 14.0 | 2086 | 1.0158 |
| 1.084 | 15.0 | 2235 | 0.9930 |
| 1.0797 | 16.0 | 2384 | 0.9855 |
| 1.0697 | 17.0 | 2533 | 1.0061 |
| 1.0652 | 18.0 | 2682 | 0.9725 |
| 1.0658 | 19.0 | 2831 | 0.9861 |
| 1.0642 | 20.0 | 2980 | 0.9919 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_es_xls-r_s118
|
jonatasgrosman
| 2022-07-11T16:13:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T16:12:22Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_xls-r_s118
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_unispeech-sat_s514
|
jonatasgrosman
| 2022-07-11T15:57:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T15:56:32Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech-sat_s514
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
nateraw/keras-dummy-functional-demo
|
nateraw
| 2022-07-11T15:41:53Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
library_name: keras
---
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 0.001 |
| decay | 0.0 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
ericntay/clinical_bert_ft
|
ericntay
| 2022-07-11T15:30:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-11T10:38:42Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: clinical_bert_ft
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. -->
# clinical_bert_ft
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2439
- F1: 0.8252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5938 | 1.0 | 95 | 0.2480 | 0.7084 |
| 0.1567 | 2.0 | 190 | 0.2035 | 0.7855 |
| 0.083 | 3.0 | 285 | 0.2002 | 0.8026 |
| 0.0482 | 4.0 | 380 | 0.2046 | 0.8118 |
| 0.0269 | 5.0 | 475 | 0.2230 | 0.8143 |
| 0.0185 | 6.0 | 570 | 0.2178 | 0.8175 |
| 0.0123 | 7.0 | 665 | 0.2269 | 0.8253 |
| 0.0093 | 8.0 | 760 | 0.2421 | 0.8227 |
| 0.0072 | 9.0 | 855 | 0.2446 | 0.8267 |
| 0.006 | 10.0 | 950 | 0.2439 | 0.8252 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ariesutiono/finetuned-test-1
|
ariesutiono
| 2022-07-11T14:57:10Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-11T13:24:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: finetuned-test-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-test-1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8192
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8219 | 1.0 | 30 | 2.3343 |
| 2.4148 | 2.0 | 60 | 2.2010 |
| 2.3236 | 3.0 | 90 | 2.1442 |
| 2.2231 | 4.0 | 120 | 2.1651 |
| 2.2171 | 5.0 | 150 | 2.0614 |
| 2.127 | 6.0 | 180 | 2.0405 |
| 2.0748 | 7.0 | 210 | 2.0092 |
| 2.0511 | 8.0 | 240 | 1.9798 |
| 2.0097 | 9.0 | 270 | 1.8662 |
| 1.9969 | 10.0 | 300 | 1.9257 |
| 2.0006 | 11.0 | 330 | 1.9386 |
| 1.9273 | 12.0 | 360 | 1.9357 |
| 1.9177 | 13.0 | 390 | 1.8983 |
| 1.9128 | 14.0 | 420 | 1.8990 |
| 1.8979 | 15.0 | 450 | 1.9037 |
| 1.8721 | 16.0 | 480 | 1.8440 |
| 1.8998 | 17.0 | 510 | 1.8404 |
| 1.8862 | 18.0 | 540 | 1.9193 |
| 1.9133 | 19.0 | 570 | 1.8494 |
| 1.8799 | 20.0 | 600 | 1.8192 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_es_vp-nl_s924
|
jonatasgrosman
| 2022-07-11T14:57:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T14:56:23Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-nl_s924
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_vp-es_s250
|
jonatasgrosman
| 2022-07-11T14:23:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T14:22:53Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-es_s250
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
huggingartists/taylor-swift
|
huggingartists
| 2022-07-11T13:52:52Z | 23 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/taylor-swift",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/taylor-swift
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/721a6c465a666419bf286b473287c33f.446x446x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">π€ HuggingArtists Model π€</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Taylor Swift</div>
<a href="https://genius.com/artists/taylor-swift">
<div style="text-align: center; font-size: 14px;">@taylor-swift</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Taylor Swift.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/taylor-swift).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/taylor-swift")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2l84tzp2/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 Taylor Swift's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1hy7aa65) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1hy7aa65/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='huggingartists/taylor-swift')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/taylor-swift")
model = AutoModelWithLMHead.from_pretrained("huggingartists/taylor-swift")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
jonatasgrosman/exp_w2v2t_es_vp-es_s859
|
jonatasgrosman
| 2022-07-11T13:12:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T13:11:34Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-es_s859
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Dudul/dudul
|
Dudul
| 2022-07-11T13:09:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-11T01:50:50Z |
---
title: Cryptopunks Generator
emoji: π§ β‘οΈπββοΈ
colorFrom: red
colorTo: indigo
sdk: gradio
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
egg22314/LaserTube
|
egg22314
| 2022-07-11T13:03:19Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-07-11T13:01:55Z |
Watching YouTube videos too boring for you? Wish you could be punished for not clicking on stuff fast enough while you watch a cat play the piano? Well, LaserTube is here to solve that problem, by letting you turn any YouTube video into a genuine simulation of an oldschool laserdisc arcade game!
Work in progress.
|
paola-md/recipe-roberta-upper-Is
|
paola-md
| 2022-07-11T12:57:29Z | 61 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-11T08:50:33Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: recipe-roberta-upper-Is
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. -->
# recipe-roberta-upper-Is
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7757
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2455 | 1.0 | 1228 | 1.0420 |
| 1.0812 | 2.0 | 2456 | 0.9641 |
| 1.018 | 3.0 | 3684 | 0.9220 |
| 0.977 | 4.0 | 4912 | 0.8943 |
| 0.9451 | 5.0 | 6140 | 0.8726 |
| 0.9254 | 6.0 | 7368 | 0.8574 |
| 0.9074 | 7.0 | 8596 | 0.8404 |
| 0.8944 | 8.0 | 9824 | 0.8290 |
| 0.8797 | 9.0 | 11052 | 0.8258 |
| 0.869 | 10.0 | 12280 | 0.8115 |
| 0.8609 | 11.0 | 13508 | 0.8085 |
| 0.8522 | 12.0 | 14736 | 0.7995 |
| 0.8462 | 13.0 | 15964 | 0.7958 |
| 0.8414 | 14.0 | 17192 | 0.7891 |
| 0.8374 | 15.0 | 18420 | 0.7856 |
| 0.8327 | 16.0 | 19648 | 0.7850 |
| 0.8268 | 17.0 | 20876 | 0.7784 |
| 0.8256 | 18.0 | 22104 | 0.7802 |
| 0.822 | 19.0 | 23332 | 0.7789 |
| 0.8219 | 20.0 | 24560 | 0.7757 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_es_vp-fr_s980
|
jonatasgrosman
| 2022-07-11T12:51:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T12:50:21Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-fr_s980
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ernestumorga/ppo-seals-Humanoid-v0
|
ernestumorga
| 2022-07-11T12:36:37Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Humanoid-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-30T12:00:35Z |
---
library_name: stable-baselines3
tags:
- seals/Humanoid-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -43.69 +/- 155.83
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Humanoid-v0
type: seals/Humanoid-v0
---
# **PPO** Agent playing **seals/Humanoid-v0**
This is a trained model of a **PPO** agent playing **seals/Humanoid-v0**
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 utils.load_from_hub --algo ppo --env seals/Humanoid-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Humanoid-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Humanoid-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Humanoid-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('clip_range', 0.2),
('ent_coef', 2.0745206045994986e-05),
('gae_lambda', 0.92),
('gamma', 0.999),
('learning_rate', 2.0309225666232827e-05),
('max_grad_norm', 0.5),
('n_envs', 1),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 10000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[256, 256], '
'vf=[256, 256])])'),
('vf_coef', 0.819262464558427),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
jonatasgrosman/exp_w2v2t_es_vp-fr_s281
|
jonatasgrosman
| 2022-07-11T12:32:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T12:31:26Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-fr_s281
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ernestumorga/sac-seals-Swimmer-v0
|
ernestumorga
| 2022-07-11T12:31:16Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Swimmer-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T12:30:14Z |
---
library_name: stable-baselines3
tags:
- seals/Swimmer-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 27.34 +/- 1.27
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Swimmer-v0
type: seals/Swimmer-v0
---
# **SAC** Agent playing **seals/Swimmer-v0**
This is a trained model of a **SAC** agent playing **seals/Swimmer-v0**
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 utils.load_from_hub --algo sac --env seals/Swimmer-v0 -orga ernestumorga -f logs/
python enjoy.py --algo sac --env seals/Swimmer-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/Swimmer-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo sac --env seals/Swimmer-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 100000),
('gamma', 0.995),
('learning_rate', 0.00039981805535514633),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(net_arch=[400, 300], log_std_init=-2.689958330139309)'),
('tau', 0.01),
('train_freq', 256),
('normalize', False)])
```
|
ernestumorga/sac-seals-Ant-v0
|
ernestumorga
| 2022-07-11T12:29:54Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Ant-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T12:28:37Z |
---
library_name: stable-baselines3
tags:
- seals/Ant-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 966.10 +/- 34.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Ant-v0
type: seals/Ant-v0
---
# **SAC** Agent playing **seals/Ant-v0**
This is a trained model of a **SAC** agent playing **seals/Ant-v0**
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 utils.load_from_hub --algo sac --env seals/Ant-v0 -orga ernestumorga -f logs/
python enjoy.py --algo sac --env seals/Ant-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/Ant-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo sac --env seals/Ant-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('buffer_size', 1000000),
('gamma', 0.98),
('learning_rate', 0.0018514039303149058),
('learning_starts', 1000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(net_arch=[256, 256], log_std_init=-2.2692589009754176)'),
('tau', 0.05),
('train_freq', 64),
('normalize', False)])
```
|
ernestumorga/sac-seals-HalfCheetah-v0
|
ernestumorga
| 2022-07-11T12:28:24Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/HalfCheetah-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-11T12:27:28Z |
---
library_name: stable-baselines3
tags:
- seals/HalfCheetah-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: SAC
results:
- metrics:
- type: mean_reward
value: 1474.73 +/- 33.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/HalfCheetah-v0
type: seals/HalfCheetah-v0
---
# **SAC** Agent playing **seals/HalfCheetah-v0**
This is a trained model of a **SAC** agent playing **seals/HalfCheetah-v0**
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 utils.load_from_hub --algo sac --env seals/HalfCheetah-v0 -orga ernestumorga -f logs/
python enjoy.py --algo sac --env seals/HalfCheetah-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo sac --env seals/HalfCheetah-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo sac --env seals/HalfCheetah-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 2048),
('buffer_size', 100000),
('gamma', 0.95),
('learning_rate', 0.000884624878315995),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(net_arch=[64, 64], log_std_init=-0.6932709443503001)'),
('tau', 0.01),
('train_freq', 64),
('normalize', False)])
```
|
ernestumorga/ppo-seals-Hopper-v0
|
ernestumorga
| 2022-07-11T12:27:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Hopper-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-30T11:59:04Z |
---
library_name: stable-baselines3
tags:
- seals/Hopper-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 2228.87 +/- 43.40
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Hopper-v0
type: seals/Hopper-v0
---
# **PPO** Agent playing **seals/Hopper-v0**
This is a trained model of a **PPO** agent playing **seals/Hopper-v0**
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 utils.load_from_hub --algo ppo --env seals/Hopper-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Hopper-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Hopper-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Hopper-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 512),
('clip_range', 0.1),
('ent_coef', 0.0010159833764878474),
('gae_lambda', 0.98),
('gamma', 0.995),
('learning_rate', 0.0003904770450788824),
('max_grad_norm', 0.9),
('n_envs', 1),
('n_epochs', 20),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[64, 64], vf=[64, '
'64])])'),
('vf_coef', 0.20315938606555833),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
ernestumorga/ppo-seals-Walker2d-v0
|
ernestumorga
| 2022-07-11T12:25:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"seals/Walker2d-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-30T10:53:25Z |
---
library_name: stable-baselines3
tags:
- seals/Walker2d-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 1429.13 +/- 411.75
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: seals/Walker2d-v0
type: seals/Walker2d-v0
---
# **PPO** Agent playing **seals/Walker2d-v0**
This is a trained model of a **PPO** agent playing **seals/Walker2d-v0**
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 utils.load_from_hub --algo ppo --env seals/Walker2d-v0 -orga ernestumorga -f logs/
python enjoy.py --algo ppo --env seals/Walker2d-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Walker2d-v0 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ppo --env seals/Walker2d-v0 -f logs/ -orga ernestumorga
```
## Hyperparameters
```python
OrderedDict([('batch_size', 8),
('clip_range', 0.4),
('ent_coef', 0.00013057334805552262),
('gae_lambda', 0.92),
('gamma', 0.98),
('learning_rate', 3.791707778339674e-05),
('max_grad_norm', 0.6),
('n_envs', 1),
('n_epochs', 5),
('n_steps', 2048),
('n_timesteps', 1000000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('policy_kwargs',
'dict(activation_fn=nn.ReLU, net_arch=[dict(pi=[256, 256], '
'vf=[256, 256])])'),
('vf_coef', 0.6167177795726859),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
jonatasgrosman/exp_w2v2t_es_vp-fr_s169
|
jonatasgrosman
| 2022-07-11T12:18:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T12:17:50Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-fr_s169
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_unispeech-ml_s952
|
jonatasgrosman
| 2022-07-11T12:05:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T12:04:48Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech-ml_s952
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_unispeech-ml_s474
|
jonatasgrosman
| 2022-07-11T11:58:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:57:35Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech-ml_s474
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_unispeech-ml_s186
|
jonatasgrosman
| 2022-07-11T11:50:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:49:25Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech-ml_s186
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
rajkumarrrk/t5-base-fine-tuned-on-cnn-dm
|
rajkumarrrk
| 2022-07-11T11:41:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-11T10:48:43Z |
---
license: apache-2.0
---
T5-base fine-tuned on CNN/DM Summarization dataset.
Training args:
```
{
"learning_rate": 0.0001,
"logging_steps": 5000,
"lr_scheduler_type": "cosine",
"num_train_epochs": 2,
"per_device_train_batch_size": 16, # total batch size of 48
"save_total_limit": 1,
"weight_decay": 0.1
}
```
Generation kwargs:
```
{
"do_sample": true,
"max_new_tokens": 100,
"min_length": 50,
"temperature": 0.7,
"top_k": 0
},
````
Pre-processing: Append prompt with prefix "Summarize: "
Post-processing: None
Test split metrics:
```
{"lexical/meteor": 0.30857827917561603,
"lexical/rouge_rouge1": 0.41099971702474514,
"lexical/rouge_rouge2": 0.17676173608661166,
"lexical/rouge_rougeL": 0.2759112075051335,
"lexical/rouge_rougeLsum": 0.34316108028094616,
"lexical/bleu": 0.10747816852428271,
"semantic/bert_score": 0.8760301497472277}
```
|
jonatasgrosman/exp_w2v2t_es_wavlm_s26
|
jonatasgrosman
| 2022-07-11T11:37:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:37:01Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_wavlm_s26
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_wavlm_s115
|
jonatasgrosman
| 2022-07-11T11:30:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:29:51Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_wavlm_s115
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_vp-sv_s93
|
jonatasgrosman
| 2022-07-11T11:11:20Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:10:33Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-sv_s93
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_vp-sv_s863
|
jonatasgrosman
| 2022-07-11T11:03:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T11:02:29Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-sv_s863
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_xlsr-53_s103
|
jonatasgrosman
| 2022-07-11T10:40:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:39:11Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_xlsr-53_s103
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_xlsr-53_s756
|
jonatasgrosman
| 2022-07-11T10:35:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:35:16Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_xlsr-53_s756
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
wooihen/distilbert-base-uncased-finetuned-emotion
|
wooihen
| 2022-07-11T10:28:32Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-11T10:04:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9225
- name: F1
type: f1
value: 0.922771245052197
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2146
- Accuracy: 0.9225
- F1: 0.9228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8233 | 1.0 | 250 | 0.3068 | 0.9025 | 0.8995 |
| 0.2394 | 2.0 | 500 | 0.2146 | 0.9225 | 0.9228 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
jonatasgrosman/exp_w2v2t_es_vp-100k_s957
|
jonatasgrosman
| 2022-07-11T10:23:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:22:18Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-100k_s957
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_wav2vec2_s875
|
jonatasgrosman
| 2022-07-11T10:19:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:18:46Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_wav2vec2_s875
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-it_s975
|
jonatasgrosman
| 2022-07-11T10:03:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T10:03:23Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-it_s975
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-it_s817
|
jonatasgrosman
| 2022-07-11T09:59:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:59:26Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-it_s817
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_r-wav2vec2_s399
|
jonatasgrosman
| 2022-07-11T09:50:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:49:58Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_r-wav2vec2_s399
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_xls-r_s635
|
jonatasgrosman
| 2022-07-11T09:42:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:42:14Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_xls-r_s635
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_unispeech-sat_s423
|
jonatasgrosman
| 2022-07-11T09:23:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:22:56Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_unispeech-sat_s423
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-es_s664
|
jonatasgrosman
| 2022-07-11T09:07:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T09:07:23Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-es_s664
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-fr_s930
|
jonatasgrosman
| 2022-07-11T08:54:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:54:29Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-fr_s930
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_wavlm_s331
|
jonatasgrosman
| 2022-07-11T08:42:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:41:54Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_wavlm_s331
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_wavlm_s116
|
jonatasgrosman
| 2022-07-11T08:39:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:38:58Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_wavlm_s116
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_no-pretraining_s895
|
jonatasgrosman
| 2022-07-11T08:30:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:29:32Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_no-pretraining_s895
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ybelkada/japanese-dummy-tokenizer
|
ybelkada
| 2022-07-11T08:24:32Z | 4 | 1 |
transformers
|
[
"transformers",
"ja",
"japanese",
"tokenizer",
"en",
"dataset:snow_simplified_japanese_corpus",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-06T12:31:37Z |
---
language:
- en
- ja
license: mit
datasets:
- snow_simplified_japanese_corpus
tags:
- ja
- japanese
- tokenizer
widget:
- text: "θͺ°γδΈηͺγ«ηγγη§γ«γ―εγγγΎγγγ"
---
# Japanese Dummy Tokenizer
Repository containing a dummy Japanese Tokenizer trained on ```snow_simplified_japanese_corpus``` dataset. The tokenizer has been trained using Hugging Face datasets in a streaming manner.
## Intended uses & limitations
You can use this tokenizer to tokenize Japanese sentences.
## How to use it
```
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ybelkada/japanese-dummy-tokenizer")
```
## How to train the tokenizer
Check the file ```tokenizer.py```, you can freely adapt it to other datasets. This tokenizer is based on the tokenizer from ```csebuetnlp/mT5_multilingual_XLSum```.
|
jonatasgrosman/exp_w2v2t_ru_vp-sv_s658
|
jonatasgrosman
| 2022-07-11T08:21:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:20:56Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-sv_s658
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-sv_s515
|
jonatasgrosman
| 2022-07-11T08:14:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:14:00Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-sv_s515
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_hubert_s732
|
jonatasgrosman
| 2022-07-11T08:10:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T08:10:28Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_hubert_s732
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
LeoFeng/superb_wav2vec_submit
|
LeoFeng
| 2022-07-11T08:05:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-11T07:57:49Z |
# SUPERB Submission Template
Welcome to the [SUPERB Challenge](https://superbbenchmark.org/challenge-slt2022/challenge_overview)! SUPERB is a collection of benchmarking resources to evaluate the capability of a universal shared representation for speech processing. It comes with a benchmark on the publicly available datasets and a challenge on a secret/not released hidden dataset. In SUPERB Challenge, a challenging hidden dataset is newly recorded to evaluate the ultimate generaliziblity across various tasks and data.
You can participate the challenge by simply submitting your self-supervised (SSL) pretrained models (model definition & pretrained weights), and we benchmark it with the hidden datasets. This repository constains useful tools to let you easliy [submit](https://superbbenchmark.org/submit) your models ***privately*** for evaluation to [the challenge hidden-set leaderboard](https://superbbenchmark.org/leaderboard?track=constrained&subset=Hidden+Dev+Set).
1. Generate a submission template
2. Validate the format/interface correctness of your model
3. Upload to Huggingface's Hub (privately)
4. Submit the upload information to [SUPERB website](https://superbbenchmark.org/submit)
#### Note 1.
We accept pre-trained models in PyTorch by default. If you wish to submit upstreams in non-PyTorch frameworks, please mail to [superb.announcement@gmail.com](mailto:superb.announcement@gmail.com)!
#### Note 2.
If you are not feasible to submit the pre-trained model, please mail to [superb.announcement@gmail.com](mailto:superb.announcement@gmail.com) for us to see how to help!
## Quickstart
### 1. Add model interfaces
#### forward
Extract features from waveforms.
- **Input:** A list of waveforms in 16000 Hz
```python
SAMPLE_RATE = 16000
BATCH_SIZE = 8
EXAMPLE_SEC = 10
wavs = [torch.randn(SAMPLE_RATE * EXAMPLE_SEC).cuda() for _ in range(BATCH_SIZE)]
```
- **Output:** A dictionary with a key "hidden_states" (for compatiblility with old ver.). The value is **a list** of padded sequences in the same shape of **(batch_size, max_sequence_length_of_batch, hidden_size)** for weighted-sum to work. It is welcome to perform some task-specified / independent pre- / post-processing on the upstream's raw hidden-sets, including upsampling and downsampling. However, all the values must come from **a single upstream model**:
```python
tasks = ["hidden_states", "PR", "SID", "ER", "ASR", "ASV", "SD", "QbE", "ST", "SS", "SE", "secret"]
for task in tasks:
# you can do task-specified pre- / post-processing depend on the arg "upstream_feature_selection"
results = upstream(wavs, upstream_feature_selection=task)
hidden_states = results["hidden_states"]
assert isinstance(results, dict)
assert isinstance(hidden_states, list)
for state in hidden_states:
assert isinstance(state, torch.Tensor)
assert state.dim() == 3, "(batch_size, max_sequence_length_of_batch, hidden_size)"
assert state.shape == hidden_states[0].shape
```
#### get_downsample_rates
Provide the downsample rate **from 16000 Hz waveforms** for each task's representation in the dict. For the standard 10ms stride representation, the downsample rate is 160.
```python
SAMPLE_RATE = 16000
MSEC_PER_SEC = 1000
downsample_rate = SAMPLE_RATE * 10 / MSEC_PER_SEC # 160
```
The downsample rate will be used to:
1. Calculate the valid representation length of each utterance in the output padded representation.
2. Prepare the training materials according to the representation's downsample rate for frame-level tasks, e.g. SD, SE, and SS.
- **Input:** the task key (str)
- **Output:** the downsample rate (int) of the representation for that task
```python
for task in tasks:
assert isinstance(task, str)
downsample_rate = upstream.get_downsample_rate(task)
assert isinstance(downsample_rate, int)
print("The upstream's representation for {task}"
f" has the downsample rate of {downsample_rate}.")
```
### 2. Create an account and organization on the Hugging Face Hub
First create an account on the Hugging Face Hub and you can sign up [here](https://huggingface.co/join) if you haven't already! Next, create a new organization and invite the SUPERB Hidden Set Committee to join. You will upload your model to a repository under this organization so that members inside it can access the model which is not publicly available.
* [superb-hidden-set](https://huggingface.co/superb-hidden-set)
### 3. Create a template repository on your machine
The next step is to create a template repository on your local machine that contains various files and a CLI to help you validate and submit your pretrained models. The Hugging Face Hub uses [Git Large File Storage (LFS)](https://git-lfs.github.com) to manage large files, so first install it if you don't have it already. For example, on macOS you can run:
```bash
brew install git-lfs
git lfs install
```
Next, run the following commands to create the repository. We recommend creating a Python virtual environment for the project, e.g. with Anaconda:
```bash
# Create and activate a virtual environment
conda create -n superb-submit python=3.8 && conda activate superb-submit
# Install the following libraries
pip install cookiecutter huggingface-hub==0.0.16
# Create the template repository
cookiecutter git+https://huggingface.co/superb/superb-submission
```
This will ask you to specify your Hugging Face Hub username, password, organisation, and the name of the repository:
```
hf_hub_username [<huggingface>]:
hf_hub_password [<password>]:
hf_hub_organisation [superb-submissions]:
repo_name [<my-superb-submissions>]:
```
This will trigger the following steps:
1. Create a private dataset repository on the Hugging Face Hub under `{hf_hub_organisation}/{repo_name}`
2. Clone the repository to your local machine
3. Add various template files, commit them locally to the repository, and push them to the Hub
The resulting repository should have the following structure:
```
my-superb-submission
βββ LICENSE
βββ README.md <- The README with submission instructions
βββ cli.py <- The CLI for validating predictions etc
βββ requirements.txt <- The requirements packages for the submissions
βββ expert.py <- Your model definition
βββ model.pt <- Your model weights
```
### 4. Install the dependencies
The final step is to install the project's dependencies:
```bash
# Navigate to the template repository
cd my-superb-submission
# Install dependencies
python -m pip install -r requirements.txt
```
That's it! You're now all set to start pretraining your speech models - see the instructions below on how to submit them to the Hub.
## Submitting to the leaderboard
To make a submission to the [leaderboard](https://superbbenchmark.org/leaderboard?subset=Hidden+Dev+Set), there are 4 main steps:
1. Modify `expert.py` and change `model.pt` so we can initialize an upstream model following the [challenge policy](https://superbbenchmark.org/challenge-slt2022/upstream) by:
```python
upstream = UpstreamExpert(ckpt="./model.pt")
```
***Package Dependency:*** Note that we only install `torch` package so far by following the above steps. If your model needs more packages, you can modify the `requirement.txt` to meet your need and install them inside the current conda environment. We will install the packages you list in the `requirement.txt` before initializing the upstream model.
2. Validate the upstream model's interface meets the requirements in the [challenge policy](https://superbbenchmark.org/challenge-slt2022/upstream). If everything is correct, you should see the following message: "All submission files validated! Now you can make a submission."
```
python cli.py validate
```
3. Push the model to the Hub! If there are no errors, you should see the following message: "Upload successful!"
```
python cli.py upload "commit message: my best model"
```
4. [Make a submission at SUPERB website](https://superbbenchmark.org/submit) by uniquely indentifying this uploaded model with the following information, which can be shown by:
```
python cli.py info
```
- Organization Name
- Repository Name
- Commit Hash (full 40 characters)
After you finish the above 4 steps. You will see a new entry in your [SUPERB profile page](https://superbbenchmark.org/profile) (need login) which does not have any benchmark numbers yet. Please wait for us to finetuned it on the hidden dataset and get the benchmark results. The results will be revealed within one week. Please stay tuned!
|
jonatasgrosman/exp_w2v2t_ru_unispeech_s132
|
jonatasgrosman
| 2022-07-11T07:58:18Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:57:53Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_unispeech_s132
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_unispeech_s42
|
jonatasgrosman
| 2022-07-11T07:55:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:54:56Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_unispeech_s42
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_xlsr-53_s201
|
jonatasgrosman
| 2022-07-11T07:49:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:48:21Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_xlsr-53_s201
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_xlsr-53_s303
|
jonatasgrosman
| 2022-07-11T07:45:29Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:44:48Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_xlsr-53_s303
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-100k_s334
|
jonatasgrosman
| 2022-07-11T07:42:16Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:41:33Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-100k_s334
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_ru_vp-100k_s732
|
jonatasgrosman
| 2022-07-11T07:39:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ru",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-11T07:38:17Z |
---
language:
- ru
license: apache-2.0
tags:
- automatic-speech-recognition
- ru
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_ru_vp-100k_s732
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Subsets and Splits
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