|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- precision |
|
- recall |
|
model-index: |
|
- name: w266_model2_BERT_LSTM_1 |
|
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. --> |
|
|
|
# w266_model2_BERT_LSTM_1 |
|
|
|
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.6673 |
|
- Accuracy: {'accuracy': 0.586} |
|
- F1: {'f1': 0.5941271393567649} |
|
- Precision: {'precision': 0.6305594263991693} |
|
- Recall: {'recall': 0.586} |
|
|
|
## 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-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: 5.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:--------------------------:|:---------------------------------:|:------------------------------:| |
|
| No log | 1.0 | 125 | 2.7886 | {'accuracy': 0.563} | {'f1': 0.5737642190234387} | {'precision': 0.6070380044002861} | {'recall': 0.563} | |
|
| No log | 2.0 | 250 | 3.2762 | {'accuracy': 0.567} | {'f1': 0.5732065475023022} | {'precision': 0.6124992011023714} | {'recall': 0.567} | |
|
| No log | 3.0 | 375 | 3.1370 | {'accuracy': 0.57} | {'f1': 0.5799666523302439} | {'precision': 0.6122839339063632} | {'recall': 0.57} | |
|
| 0.0465 | 4.0 | 500 | 3.3590 | {'accuracy': 0.569} | {'f1': 0.5796357806282344} | {'precision': 0.6093440842818532} | {'recall': 0.5689999999999998} | |
|
| 0.0465 | 5.0 | 625 | 3.4285 | {'accuracy': 0.57} | {'f1': 0.580483223593091} | {'precision': 0.618976915416096} | {'recall': 0.57} | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.31.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.2 |
|
- Tokenizers 0.13.3 |
|
|
