Instructions to use Flamgrise/bios_lol_fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Flamgrise/bios_lol_fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Flamgrise/bios_lol_fine-tuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Flamgrise/bios_lol_fine-tuned") model = AutoModelForSequenceClassification.from_pretrained("Flamgrise/bios_lol_fine-tuned") - Notebooks
- Google Colab
- Kaggle
bios_lol_fine-tuned
This model is a fine-tuned version of facebook/bart-large-mnli on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9271
- F1: 0.1383
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 44 | 1.8027 | 0.0870 |
| No log | 2.0 | 88 | 1.8250 | 0.1652 |
| No log | 3.0 | 132 | 1.9272 | 0.2199 |
| No log | 4.0 | 176 | 1.9271 | 0.1383 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for Flamgrise/bios_lol_fine-tuned
Base model
facebook/bart-large-mnli