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---
license: apache-2.0
base_model: surrey-nlp/albert-large-v2-finetuned-abbDet
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-large-v2-finetuned-abbDet-finetuned-ner
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. -->
# albert-large-v2-finetuned-abbDet-finetuned-ner
This model is a fine-tuned version of [surrey-nlp/albert-large-v2-finetuned-abbDet](https://huggingface.co/surrey-nlp/albert-large-v2-finetuned-abbDet) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0950
- Precision: 0.9784
- Recall: 0.9763
- F1: 0.9773
- Accuracy: 0.9757
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.37 | 100 | 0.1655 | 0.9638 | 0.9621 | 0.9629 | 0.9622 |
| No log | 0.75 | 200 | 0.1073 | 0.9752 | 0.9705 | 0.9729 | 0.9709 |
| No log | 1.12 | 300 | 0.0951 | 0.9776 | 0.9742 | 0.9759 | 0.9740 |
| No log | 1.49 | 400 | 0.0952 | 0.9778 | 0.9752 | 0.9765 | 0.9748 |
| 0.1901 | 1.87 | 500 | 0.0948 | 0.9780 | 0.9745 | 0.9763 | 0.9746 |
| 0.1901 | 2.24 | 600 | 0.0947 | 0.9788 | 0.9758 | 0.9773 | 0.9755 |
| 0.1901 | 2.61 | 700 | 0.0962 | 0.9789 | 0.9766 | 0.9778 | 0.9758 |
| 0.1901 | 2.99 | 800 | 0.0950 | 0.9784 | 0.9763 | 0.9773 | 0.9757 |
| 0.1901 | 3.36 | 900 | 0.0984 | 0.9784 | 0.9763 | 0.9773 | 0.9755 |
| 0.0493 | 3.73 | 1000 | 0.1012 | 0.9781 | 0.9759 | 0.9770 | 0.9752 |
| 0.0493 | 4.1 | 1100 | 0.1029 | 0.9781 | 0.9763 | 0.9772 | 0.9754 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
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