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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- surrey-nlp/PLOD-unfiltered
metrics:
- precision
- recall
- f1
- accuracy
language:
- en
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)."
model-index:
- name: albert-large-v2-finetuned-ner_with_callbacks
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: surrey-nlp/PLOD-unfiltered
type: token-classification
args: PLODunfiltered
metrics:
- name: Precision
type: precision
value: 0.9655166719570215
- name: Recall
type: recall
value: 0.9608483288141474
- name: F1
type: f1
value: 0.9631768437660728
- name: Accuracy
type: accuracy
value: 0.9589410429715819
---
<!-- 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
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