Instructions to use npark95/finetuned_ClinicalLongformer_CAT_020425 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npark95/finetuned_ClinicalLongformer_CAT_020425 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="npark95/finetuned_ClinicalLongformer_CAT_020425")# 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") - Notebooks
- Google Colab
- Kaggle
File size: 1,540 Bytes
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library_name: transformers
base_model: yikuan8/Clinical-Longformer
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: finetuned_ClinicalLongformer_CAT_020425
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_ClinicalLongformer_CAT_020425
This model is a fine-tuned version of [yikuan8/Clinical-Longformer](https://huggingface.co/yikuan8/Clinical-Longformer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0446
- F1: 0.9932
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0027 | 1.0 | 424 | 0.0446 | 0.9932 |
| 0.0079 | 2.0 | 848 | 0.0495 | 0.9918 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|