|
--- |
|
license: apache-2.0 |
|
base_model: ViktorDo/EcoBERT-Pretrained |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: EcoBERT-finetuned-ner-copious |
|
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. --> |
|
|
|
# EcoBERT-finetuned-ner-copious |
|
|
|
This model is a fine-tuned version of [ViktorDo/EcoBERT-Pretrained](https://huggingface.co/ViktorDo/EcoBERT-Pretrained) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0731 |
|
- Precision: 0.6213 |
|
- Recall: 0.6754 |
|
- F1: 0.6472 |
|
- Accuracy: 0.9761 |
|
|
|
## 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: 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 | 1.0 | 63 | 0.1220 | 0.3423 | 0.3681 | 0.3547 | 0.9567 | |
|
| No log | 2.0 | 126 | 0.0837 | 0.5305 | 0.5797 | 0.5540 | 0.9714 | |
|
| No log | 3.0 | 189 | 0.0751 | 0.5950 | 0.6261 | 0.6102 | 0.9749 | |
|
| No log | 4.0 | 252 | 0.0729 | 0.6118 | 0.6623 | 0.6360 | 0.9763 | |
|
| No log | 5.0 | 315 | 0.0731 | 0.6213 | 0.6754 | 0.6472 | 0.9761 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.2 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|