---
library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
model-index:
- name: scibert-acm-multilabel
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scibert-acm-multilabel

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0238
- F1 Average Per Sample: 0.4069

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1 Average Per Sample |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------:|
| 0.0565        | 1.0   | 5000  | 0.0291          | 0.2295                |
| 0.0513        | 2.0   | 10000 | 0.0251          | 0.3690                |
| 0.0459        | 3.0   | 15000 | 0.0240          | 0.3994                |
| 0.0443        | 4.0   | 20000 | 0.0238          | 0.4069                |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2