|
|
--- |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
- precision |
|
|
- recall |
|
|
model-index: |
|
|
- name: sentiment_pred_24feb |
|
|
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. --> |
|
|
|
|
|
# sentiment_pred_24feb |
|
|
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.2754 |
|
|
- Y True: [1 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 0 1 1 1 0 1 |
|
|
1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 0 0 0 1 0 |
|
|
1 1 0 1 1 0 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 0 0 1 1 |
|
|
1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 1 1 1 |
|
|
1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 |
|
|
1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1 |
|
|
1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 |
|
|
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 |
|
|
0 0 0] |
|
|
- Y Pred: [0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 |
|
|
1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 0 0 0 1 0 |
|
|
1 1 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 1 0 0 1 1 |
|
|
1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 1 1 1 |
|
|
1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 1 1 |
|
|
1 1 0 1 0 1 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 0 |
|
|
1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 |
|
|
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 |
|
|
0 0 0] |
|
|
- Accuracy: 0.9298 |
|
|
- F1: 0.9297 |
|
|
- Precision: 0.9313 |
|
|
- Recall: 0.9298 |
|
|
- Confusion Matrix: [[145 6] |
|
|
[ 15 133]] |
|
|
|
|
|
## 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: linear |
|
|
- lr_scheduler_warmup_steps: 50 |
|
|
- num_epochs: 5 |
|
|
|
|
|
### Training results |
|
|
|
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.14.1 |
|
|
- Pytorch 1.13.1+cu117 |
|
|
- Datasets 1.16.1 |
|
|
- Tokenizers 0.10.3 |
|
|
|
