Instructions to use btamm12/roberta-base-finetuned-wls-whisper-5ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use btamm12/roberta-base-finetuned-wls-whisper-5ep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="btamm12/roberta-base-finetuned-wls-whisper-5ep")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("btamm12/roberta-base-finetuned-wls-whisper-5ep") model = AutoModelForMaskedLM.from_pretrained("btamm12/roberta-base-finetuned-wls-whisper-5ep") - Notebooks
- Google Colab
- Kaggle
roberta-base-finetuned-wls-whisper-5ep
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0709
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4616 | 1.0 | 26 | 1.2511 |
| 1.2324 | 2.0 | 52 | 1.1991 |
| 1.157 | 3.0 | 78 | 1.1376 |
| 1.1137 | 4.0 | 104 | 1.1066 |
| 1.0555 | 5.0 | 130 | 1.1292 |
Framework versions
- Transformers 4.31.0
- Pytorch 1.11.0+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for btamm12/roberta-base-finetuned-wls-whisper-5ep
Base model
FacebookAI/roberta-base