Instructions to use rexarski/distilroberta-tcfd-disclosure-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rexarski/distilroberta-tcfd-disclosure-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rexarski/distilroberta-tcfd-disclosure-5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rexarski/distilroberta-tcfd-disclosure-5") model = AutoModelForSequenceClassification.from_pretrained("rexarski/distilroberta-tcfd-disclosure-5") - Notebooks
- Google Colab
- Kaggle
distilroberta-tcfd-disclosure-5
This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5761
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6682 | 1.0 | 363 | 0.6726 |
| 0.6242 | 2.0 | 727 | 0.5921 |
| 0.4459 | 3.0 | 1090 | 0.5238 |
| 0.3456 | 4.0 | 1454 | 0.5268 |
| 0.2991 | 5.0 | 1817 | 0.5761 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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