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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: IKT_classifier_economywide_best
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. -->
# IKT_classifier_economywide_best
This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1642
- Precision Weighted: 0.9530
- Precision Macro: 0.9524
- Recall Weighted: 0.9528
- Recall Samples: 0.9532
- F1-score: 0.9527
- Accuracy: 0.9528
## 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: 9.375102561418467e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100.0
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Weighted | Precision Macro | Recall Weighted | Recall Samples | F1-score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:---------------:|:--------------:|:--------:|:--------:|
| No log | 1.0 | 30 | 0.3847 | 0.9356 | 0.9340 | 0.9340 | 0.9354 | 0.9339 | 0.9340 |
| No log | 2.0 | 60 | 0.3545 | 0.8911 | 0.8933 | 0.8868 | 0.8832 | 0.8853 | 0.8868 |
| No log | 3.0 | 90 | 0.1387 | 0.9623 | 0.9621 | 0.9623 | 0.9621 | 0.9621 | 0.9623 |
| No log | 4.0 | 120 | 0.1840 | 0.9541 | 0.9555 | 0.9528 | 0.9511 | 0.9525 | 0.9528 |
| No log | 5.0 | 150 | 0.1642 | 0.9530 | 0.9524 | 0.9528 | 0.9532 | 0.9527 | 0.9528 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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