Instructions to use DazMashaly/new_downloads with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DazMashaly/new_downloads with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DazMashaly/new_downloads")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("DazMashaly/new_downloads") model = AutoModelForSpeechSeq2Seq.from_pretrained("DazMashaly/new_downloads", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 5
Browse files- README.md +11 -12
- config.json +1 -1
- model.safetensors +1 -1
- runs/Jun25_06-08-05_557d2a8f2caf/events.out.tfevents.1719295686.557d2a8f2caf.34.0 +3 -0
- training_args.bin +1 -1
README.md
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---
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base_model: DazMashaly/output
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tags:
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- generated_from_trainer
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model-index:
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- name:
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results: []
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---
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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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#
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This model
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It achieves the following results on the evaluation set:
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- eval_loss: 5.
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- eval_wer: 1.0
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- eval_cer: 1.0
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- eval_runtime:
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- eval_samples_per_second:
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- eval_steps_per_second: 0.
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- epoch:
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size:
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- eval_batch_size: 128
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- Transformers 4.41.2
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- Pytorch 2.1.2
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- Datasets 2.
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- Tokenizers 0.19.1
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: output
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results: []
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---
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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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# output
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 5.5136
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- eval_wer: 1.0
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- eval_cer: 1.0
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- eval_runtime: 702.4432
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- eval_samples_per_second: 3.131
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- eval_steps_per_second: 0.026
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- epoch: 2.0
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- step: 818
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 124
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- eval_batch_size: 128
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- Transformers 4.41.2
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- Pytorch 2.1.2
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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config.json
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{
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"_name_or_path": "DazMashaly/
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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{
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"_name_or_path": "DazMashaly/new_downloads",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 290403936
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc48c0adcc1e3b3c0bd007518f406e2619082456c1687e6f499e09a28926a2b2
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size 290403936
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runs/Jun25_06-08-05_557d2a8f2caf/events.out.tfevents.1719295686.557d2a8f2caf.34.0
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version https://git-lfs.github.com/spec/v1
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size 10374
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training_args.bin
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size 5304
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version https://git-lfs.github.com/spec/v1
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size 5304
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