|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: electra-base-ner-food-recipe-v2 |
|
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. --> |
|
|
|
# electra-base-ner-food-recipe-v2 |
|
|
|
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1500 |
|
- Precision: 0.7191 |
|
- Recall: 0.8739 |
|
- F1: 0.7890 |
|
- Accuracy: 0.9568 |
|
|
|
## 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: 5e-07 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 0.5 | 400 | 0.4360 | 0.4354 | 0.7533 | 0.5519 | 0.8775 | |
|
| 0.5627 | 1.01 | 800 | 0.2274 | 0.6971 | 0.8525 | 0.7670 | 0.9508 | |
|
| 0.2799 | 1.51 | 1200 | 0.1791 | 0.6728 | 0.8762 | 0.7612 | 0.9492 | |
|
| 0.1983 | 2.01 | 1600 | 0.1652 | 0.6958 | 0.8757 | 0.7755 | 0.9535 | |
|
| 0.1821 | 2.51 | 2000 | 0.1610 | 0.7171 | 0.8766 | 0.7889 | 0.9568 | |
|
| 0.1821 | 3.02 | 2400 | 0.1550 | 0.7001 | 0.8757 | 0.7782 | 0.9539 | |
|
| 0.1726 | 3.52 | 2800 | 0.1537 | 0.7211 | 0.8744 | 0.7904 | 0.9573 | |
|
| 0.1674 | 4.02 | 3200 | 0.1510 | 0.7170 | 0.8739 | 0.7877 | 0.9565 | |
|
| 0.1682 | 4.52 | 3600 | 0.1501 | 0.7147 | 0.8744 | 0.7865 | 0.9564 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.4 |
|
- Pytorch 2.0.0+cu118 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.3 |
|
|
