# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("npark95/finetuned_ClinicalLongformer_CAT_020425")
model = AutoModelForSequenceClassification.from_pretrained("npark95/finetuned_ClinicalLongformer_CAT_020425")Quick Links
finetuned_ClinicalLongformer_CAT_020425
This model is a fine-tuned version of yikuan8/Clinical-Longformer on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0433
- F1: 0.9939
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.0142 | 1.0 | 424 | 0.0427 | 0.9912 |
| 0.0072 | 2.0 | 848 | 0.0433 | 0.9939 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 6
Model tree for npark95/finetuned_ClinicalLongformer_CAT_020425
Base model
yikuan8/Clinical-Longformer
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="npark95/finetuned_ClinicalLongformer_CAT_020425")