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---
license: mit
base_model: MoritzLaurer/bge-m3-zeroshot-v2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: nli-finetuning-laurer-immigration-classification
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. -->
# nli-finetuning-laurer-immigration-classification
This model is a fine-tuned version of [MoritzLaurer/bge-m3-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5578
- Accuracy: 0.9032
- F1 Macro: 0.8969
- Accuracy Balanced: 0.8913
- F1 Micro: 0.9032
- Precision Macro: 0.9048
- Recall Macro: 0.8913
- Precision Micro: 0.9032
- Recall Micro: 0.9032
## 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: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| No log | 1.0 | 151 | 0.3342 | 0.8763 | 0.8679 | 0.8619 | 0.8763 | 0.8769 | 0.8619 | 0.8763 | 0.8763 |
| No log | 2.0 | 302 | 0.4733 | 0.8710 | 0.8680 | 0.8793 | 0.8710 | 0.8644 | 0.8793 | 0.8710 | 0.8710 |
| No log | 3.0 | 453 | 0.5168 | 0.8978 | 0.8895 | 0.8796 | 0.8978 | 0.9073 | 0.8796 | 0.8978 | 0.8978 |
| 0.4084 | 4.0 | 604 | 0.5300 | 0.8871 | 0.8813 | 0.8804 | 0.8871 | 0.8823 | 0.8804 | 0.8871 | 0.8871 |
| 0.4084 | 5.0 | 755 | 0.5578 | 0.9032 | 0.8969 | 0.8913 | 0.9032 | 0.9048 | 0.8913 | 0.9032 | 0.9032 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.5.0+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3
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