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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-large-v2_ner_wnut_17
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.7445742904841403
- name: Recall
type: recall
value: 0.5334928229665071
- name: F1
type: f1
value: 0.621602787456446
- name: Accuracy
type: accuracy
value: 0.9581637843336724
---
<!-- 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_ner_wnut_17
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2429
- Precision: 0.7446
- Recall: 0.5335
- F1: 0.6216
- Accuracy: 0.9582
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.3051 | 0.7929 | 0.3206 | 0.4566 | 0.9410 |
| No log | 2.0 | 426 | 0.2151 | 0.7443 | 0.4665 | 0.5735 | 0.9516 |
| 0.17 | 3.0 | 639 | 0.2310 | 0.7364 | 0.5012 | 0.5964 | 0.9559 |
| 0.17 | 4.0 | 852 | 0.2387 | 0.7564 | 0.5311 | 0.6240 | 0.9578 |
| 0.0587 | 5.0 | 1065 | 0.2429 | 0.7446 | 0.5335 | 0.6216 | 0.9582 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1