---
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