---
license: apache-2.0
base_model: jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE
tags:
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: bert-base-uncased-MLP-scirepeval-chemistry-LARGE-textCLS-RHEOLOGY-20230913-3
  results: []
datasets:
- bluesky333/chemical_language_understanding_benchmark
language:
- en
---

<!-- 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. -->

# bert-base-uncased-MLP-scirepeval-chemistry-LARGE-textCLS-RHEOLOGY-20230913-3

This model is a fine-tuned version of [jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE](https://huggingface.co/jonas-luehrs/bert-base-uncased-MLP-scirepeval-chemistry-LARGE) on the RHEOLOGY dataset of the [blue333/chemical_language_understanding_benchmark](https://huggingface.co/datasets/bluesky333/chemical_language_understanding_benchmark).
It achieves the following results on the evaluation set:
- Loss: 0.6836
- F1: 0.7805
- Precision: 0.7860
- Recall: 0.7840
- Accuracy: 0.7840

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 1.1777        | 1.0   | 46   | 0.8465          | 0.6593 | 0.6346    | 0.7037 | 0.7037   |
| 0.6923        | 2.0   | 92   | 0.7123          | 0.7491 | 0.7654    | 0.7593 | 0.7593   |
| 0.4974        | 3.0   | 138  | 0.6906          | 0.7563 | 0.7667    | 0.7593 | 0.7593   |
| 0.3789        | 4.0   | 184  | 0.6754          | 0.7645 | 0.7712    | 0.7716 | 0.7716   |
| 0.3053        | 5.0   | 230  | 0.6836          | 0.7805 | 0.7860    | 0.7840 | 0.7840   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3