---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- surrey-nlp/PLOD-unfiltered
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons.
- text: RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory
    cortex in Figure 1.
- text: Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar
    imaging (EPI).
base_model: albert-large-v2
model-index:
- name: albert-large-v2-finetuned-ner_with_callbacks
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: surrey-nlp/PLOD-unfiltered
      type: token-classification
      args: PLODunfiltered
    metrics:
    - type: precision
      value: 0.9655166719570215
      name: Precision
    - type: recall
      value: 0.9608483288141474
      name: Recall
    - type: f1
      value: 0.9631768437660728
      name: F1
    - type: accuracy
      value: 0.9589410429715819
      name: Accuracy
---

<!-- 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-ner_with_callbacks

This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the [PLOD-unfiltered](https://huggingface.co/datasets/surrey-nlp/PLOD-unfiltered) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1235
- Precision: 0.9655
- Recall: 0.9608
- F1: 0.9632
- Accuracy: 0.9589

## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1377        | 0.49  | 7000  | 0.1294          | 0.9563    | 0.9422 | 0.9492 | 0.9436   |
| 0.1244        | 0.98  | 14000 | 0.1165          | 0.9589    | 0.9504 | 0.9546 | 0.9499   |
| 0.107         | 1.48  | 21000 | 0.1140          | 0.9603    | 0.9509 | 0.9556 | 0.9511   |
| 0.1088        | 1.97  | 28000 | 0.1086          | 0.9613    | 0.9551 | 0.9582 | 0.9536   |
| 0.0918        | 2.46  | 35000 | 0.1059          | 0.9617    | 0.9582 | 0.9600 | 0.9556   |
| 0.0847        | 2.95  | 42000 | 0.1067          | 0.9620    | 0.9586 | 0.9603 | 0.9559   |
| 0.0734        | 3.44  | 49000 | 0.1188          | 0.9646    | 0.9588 | 0.9617 | 0.9574   |
| 0.0725        | 3.93  | 56000 | 0.1065          | 0.9660    | 0.9599 | 0.9630 | 0.9588   |
| 0.0547        | 4.43  | 63000 | 0.1273          | 0.9662    | 0.9602 | 0.9632 | 0.9590   |
| 0.0542        | 4.92  | 70000 | 0.1235          | 0.9655    | 0.9608 | 0.9632 | 0.9589   |
| 0.0374        | 5.41  | 77000 | 0.1401          | 0.9647    | 0.9613 | 0.9630 | 0.9586   |
| 0.0417        | 5.9   | 84000 | 0.1380          | 0.9641    | 0.9622 | 0.9632 | 0.9588   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.1+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1