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---
license: apache-2.0
base_model: alex-miller/ODABert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: multi-dimensional-disability
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. -->
# multi-dimensional-disability
This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6084
- Accuracy: 0.9116
- F1: 0.8471
- Precision: 0.8029
- Recall: 0.8966
## 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: 1e-06
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.9604 | 1.0 | 437 | 0.9473 | 0.8617 | 0.7483 | 0.7446 | 0.7519 |
| 0.8897 | 2.0 | 874 | 0.8169 | 0.8804 | 0.7995 | 0.7380 | 0.8721 |
| 0.8054 | 3.0 | 1311 | 0.7170 | 0.8858 | 0.8028 | 0.7601 | 0.8505 |
| 0.6757 | 4.0 | 1748 | 0.6679 | 0.8921 | 0.8164 | 0.7631 | 0.8777 |
| 0.6153 | 5.0 | 2185 | 0.6279 | 0.8992 | 0.8275 | 0.7772 | 0.8847 |
| 0.5782 | 6.0 | 2622 | 0.5773 | 0.8995 | 0.8305 | 0.7705 | 0.9008 |
| 0.5435 | 7.0 | 3059 | 0.6106 | 0.9072 | 0.8401 | 0.7937 | 0.8924 |
| 0.5333 | 8.0 | 3496 | 0.6141 | 0.9079 | 0.8435 | 0.7878 | 0.9078 |
| 0.5349 | 9.0 | 3933 | 0.6056 | 0.9097 | 0.8448 | 0.7964 | 0.8994 |
| 0.539 | 10.0 | 4370 | 0.6084 | 0.9116 | 0.8471 | 0.8029 | 0.8966 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.4.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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