Training in progress, epoch 1
Browse files- README.md +66 -179
- all_results.json +13 -0
- eval_results.json +8 -0
- log_history.json +1928 -0
- model.safetensors +1 -1
- train_results.json +8 -0
- trainer_state.json +1959 -0
- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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---
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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#### Factors
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[More Information Needed]
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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---
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library_name: transformers
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tags:
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- audio-classification
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- generated_from_trainer
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datasets:
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- voxceleb
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metrics:
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- accuracy
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model-index:
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- name: ce-len3-bs256-lr1e-3
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: confit/voxceleb
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type: voxceleb
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config: verification
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split: train
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args: verification
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9410023545240498
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# ce-len3-bs256-lr1e-3
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This model is a fine-tuned version of [](https://huggingface.co/) on the confit/voxceleb dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2946
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- Accuracy: 0.9410
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.001
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- train_batch_size: 256
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- eval_batch_size: 1
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- seed: 914
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 10.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 4.6728 | 1.0 | 523 | 4.3456 | 0.1504 |
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| 3.224 | 2.0 | 1046 | 2.2589 | 0.5141 |
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| 2.3964 | 3.0 | 1569 | 1.4663 | 0.6836 |
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| 1.8474 | 4.0 | 2092 | 0.9548 | 0.7927 |
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| 1.5275 | 5.0 | 2615 | 0.6698 | 0.8571 |
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| 1.248 | 6.0 | 3138 | 0.5270 | 0.8899 |
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| 1.0991 | 7.0 | 3661 | 0.4500 | 0.9037 |
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| 0.9221 | 8.0 | 4184 | 0.3572 | 0.9267 |
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| 0.7997 | 9.0 | 4707 | 0.3138 | 0.9353 |
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| 0.7603 | 10.0 | 5230 | 0.2946 | 0.9410 |
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### Framework versions
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- Transformers 4.48.3
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- Pytorch 2.0.0+cu117
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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all_results.json
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{
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"epoch": 10.0,
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"eval_accuracy": 0.9410023545240498,
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"eval_loss": 0.29460111260414124,
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"eval_runtime": 49.9476,
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"eval_samples_per_second": 297.612,
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"eval_steps_per_second": 297.612,
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"total_flos": 1.96318398191328e+18,
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"train_loss": 2.1099621225725396,
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"train_runtime": 19361.582,
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"train_samples_per_second": 69.094,
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"train_steps_per_second": 0.27
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}
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{
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"epoch": 10.0,
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"eval_accuracy": 0.9410023545240498,
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"eval_loss": 0.29460111260414124,
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"eval_runtime": 49.9476,
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"eval_samples_per_second": 297.612,
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"eval_steps_per_second": 297.612
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}
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|
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|
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version https://git-lfs.github.com/spec/v1
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train_results.json
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|
trainer_state.json
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