metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased_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: 0.6700879765395894
- name: Recall
type: recall
value: 0.5466507177033493
- name: F1
type: f1
value: 0.6021080368906456
- name: Accuracy
type: accuracy
value: 0.9559412550066756
distilbert-base-uncased_ner_wnut_17
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2400
- Precision: 0.6701
- Recall: 0.5467
- F1: 0.6021
- Accuracy: 0.9559
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 | 0.2367 | 0.6879 | 0.4270 | 0.5269 | 0.9455 |
| No log | 2.0 | 426 | 0.2272 | 0.6913 | 0.4928 | 0.5754 | 0.9533 |
| 0.173 | 3.0 | 639 | 0.2393 | 0.6788 | 0.5132 | 0.5845 | 0.9553 |
| 0.173 | 4.0 | 852 | 0.2338 | 0.6541 | 0.5610 | 0.6040 | 0.9557 |
| 0.0489 | 5.0 | 1065 | 0.2400 | 0.6701 | 0.5467 | 0.6021 | 0.9559 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1