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library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: section-classifier-imrad
results: []
datasets:
- saier/unarXive_imrad_clf
---
<!-- 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. -->
# section-classifier-imrad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [saier/unarXive_imrad_clf](https://huggingface.co/datasets/saier/unarXive_imrad_clf) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6404
- Accuracy: 0.7714
- F1: 0.7760
- Precision: 0.7891
- Recall: 0.7714
## Model description
This model classifies scientific paper sections into IMRaD categories (Introduction, Methods, Results, and Discussion). It's a fine-tuned version of DistilBERT trained on the unarXive dataset with weighted cross-entropy loss to handle class imbalance.
## Intended uses & limitations
Intended use: Automatically categorizing sections in academic papers, particularly arXiv submissions.
Limitations: Trained exclusively on arXiv papers; may not generalize well to non-academic text or from other domains. Requires text segments of reasonable length (up to 512 tokens).
## Training and evaluation data
Trained on saier/unarXive_imrad_clf, a dataset of labeled paper sections from arXiv. The model uses weighted class balancing to account for label distribution imbalance across the five IMRaD categories.
## How to use
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "your-username/section-classifier-imrad"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
texts = [
"In this paper, we propose a new method for retrieval.",
"We evaluate on three benchmarks and report state-of-the-art results."
]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
pred_ids = torch.argmax(logits, dim=-1).tolist()
id2label = model.config.id2label
for t, i in zip(texts, pred_ids):
print(id2label[i], ":", t)
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5822 | 0.0062 | 100 | 1.5712 | 0.3785 | 0.3802 | 0.5100 | 0.3785 |
| 1.2276 | 0.0123 | 200 | 1.1797 | 0.3953 | 0.3201 | 0.5746 | 0.3953 |
| 1.0040 | 0.0185 | 300 | 1.0034 | 0.5159 | 0.5110 | 0.6109 | 0.5159 |
| 0.8683 | 0.0246 | 400 | 0.8951 | 0.5797 | 0.5820 | 0.6596 | 0.5797 |
| 0.9856 | 0.0308 | 500 | 0.8343 | 0.6607 | 0.6648 | 0.6940 | 0.6607 |
| 0.8125 | 0.0369 | 600 | 0.8134 | 0.6559 | 0.6609 | 0.7009 | 0.6559 |
| 0.8667 | 0.0431 | 700 | 0.7731 | 0.6905 | 0.6956 | 0.7283 | 0.6905 |
| 0.8262 | 0.0492 | 800 | 0.7533 | 0.6881 | 0.6957 | 0.7343 | 0.6881 |
| 0.7894 | 0.0554 | 900 | 0.7523 | 0.6379 | 0.6419 | 0.7273 | 0.6379 |
| 0.7810 | 0.0615 | 1000 | 0.7639 | 0.6919 | 0.7023 | 0.7349 | 0.6919 |
| 0.7102 | 0.0677 | 1100 | 0.7708 | 0.7163 | 0.7207 | 0.7467 | 0.7163 |
| 0.6794 | 0.0738 | 1200 | 0.7344 | 0.7057 | 0.7147 | 0.7469 | 0.7057 |
| 0.7838 | 0.0800 | 1300 | 0.7484 | 0.7133 | 0.7188 | 0.7467 | 0.7133 |
| 0.7457 | 0.0861 | 1400 | 0.7024 | 0.6845 | 0.6910 | 0.7501 | 0.6845 |
| 0.6696 | 0.0923 | 1500 | 0.7355 | 0.6763 | 0.6867 | 0.7516 | 0.6763 |
| 0.5735 | 0.0984 | 1600 | 0.7082 | 0.7231 | 0.7305 | 0.7575 | 0.7231 |
| 0.7231 | 0.1046 | 1700 | 0.6850 | 0.7253 | 0.7303 | 0.7529 | 0.7253 |
| 0.7180 | 0.1108 | 1800 | 0.7049 | 0.7039 | 0.7120 | 0.7554 | 0.7039 |
| 0.7093 | 0.1169 | 1900 | 0.7192 | 0.6841 | 0.6919 | 0.7533 | 0.6841 |
| 0.6047 | 0.1231 | 2000 | 0.6679 | 0.7407 | 0.7459 | 0.7639 | 0.7407 |
| 0.6954 | 0.1292 | 2100 | 0.7083 | 0.7237 | 0.7329 | 0.7616 | 0.7237 |
| 0.6577 | 0.1354 | 2200 | 0.6808 | 0.7215 | 0.7278 | 0.7583 | 0.7215 |
| 0.6743 | 0.1415 | 2300 | 0.6904 | 0.7251 | 0.7338 | 0.7682 | 0.7251 |
| 0.5870 | 0.1477 | 2400 | 0.6747 | 0.7217 | 0.7301 | 0.7728 | 0.7217 |
| 0.6079 | 0.1538 | 2500 | 0.6609 | 0.7502 | 0.7563 | 0.7745 | 0.7502 |
| 0.5927 | 0.1600 | 2600 | 0.6757 | 0.7485 | 0.7544 | 0.7698 | 0.7485 |
| 0.6936 | 0.1661 | 2700 | 0.6970 | 0.7548 | 0.7606 | 0.7769 | 0.7548 |
| 0.7466 | 0.1723 | 2800 | 0.6619 | 0.7401 | 0.7475 | 0.7726 | 0.7401 |
| 0.7301 | 0.1784 | 2900 | 0.6474 | 0.7337 | 0.7404 | 0.7691 | 0.7337 |
| 0.6256 | 0.1846 | 3000 | 0.6474 | 0.7381 | 0.7456 | 0.7733 | 0.7381 |
| 0.7141 | 0.1907 | 3100 | 0.7102 | 0.7231 | 0.7360 | 0.7727 | 0.7231 |
| 0.6770 | 0.1969 | 3200 | 0.6436 | 0.7177 | 0.7233 | 0.7651 | 0.7177 |
| 0.7148 | 0.2031 | 3300 | 0.6410 | 0.7493 | 0.7560 | 0.7775 | 0.7493 |
| 0.6010 | 0.2092 | 3400 | 0.6683 | 0.7626 | 0.7667 | 0.7773 | 0.7626 |
| 0.7568 | 0.2154 | 3500 | 0.6563 | 0.7590 | 0.7660 | 0.7836 | 0.7590 |
| 0.6437 | 0.2215 | 3600 | 0.6377 | 0.7419 | 0.7504 | 0.7839 | 0.7419 |
| 0.7817 | 0.2277 | 3700 | 0.6439 | 0.7487 | 0.7560 | 0.7814 | 0.7487 |
| 0.6606 | 0.2338 | 3800 | 0.6534 | 0.7534 | 0.7603 | 0.7821 | 0.7534 |
| 0.6466 | 0.2400 | 3900 | 0.6859 | 0.7063 | 0.7167 | 0.7661 | 0.7063 |
| 0.6616 | 0.2461 | 4000 | 0.6461 | 0.7217 | 0.7307 | 0.7775 | 0.7217 |
| 0.6033 | 0.2523 | 4100 | 0.6394 | 0.7419 | 0.7490 | 0.7761 | 0.7419 |
| 0.6647 | 0.2584 | 4200 | 0.6229 | 0.7680 | 0.7722 | 0.7833 | 0.7680 |
| 0.7093 | 0.2646 | 4300 | 0.6309 | 0.7419 | 0.7488 | 0.7752 | 0.7419 |
| 0.6773 | 0.2707 | 4400 | 0.6342 | 0.7594 | 0.7651 | 0.7817 | 0.7594 |
| 0.6944 | 0.2769 | 4500 | 0.6363 | 0.7522 | 0.7588 | 0.7821 | 0.7522 |
| 0.5588 | 0.2830 | 4600 | 0.6503 | 0.7431 | 0.7516 | 0.7838 | 0.7431 |
| 0.6522 | 0.2892 | 4700 | 0.6412 | 0.7526 | 0.7589 | 0.7783 | 0.7526 |
| 0.6321 | 0.2953 | 4800 | 0.6569 | 0.7666 | 0.7727 | 0.7914 | 0.7666 |
| 0.6983 | 0.3015 | 4900 | 0.6327 | 0.7339 | 0.7414 | 0.7767 | 0.7339 |
| 0.6051 | 0.3077 | 5000 | 0.6754 | 0.7229 | 0.7340 | 0.7752 | 0.7229 |
| 0.7185 | 0.3138 | 5100 | 0.6220 | 0.7532 | 0.7590 | 0.7809 | 0.7532 |
| 0.7003 | 0.3200 | 5200 | 0.6200 | 0.7413 | 0.7479 | 0.7788 | 0.7413 |
### Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.1
- Tokenizers 0.22.2 |