---
base_model: xhub/cmi-intent-classifier
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
- recall
model-index:
- name: cmi-intent-classifier-v1
  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. -->

# cmi-intent-classifier-v1

This model is a fine-tuned version of [xhub/cmi-intent-classifier](https://huggingface.co/xhub/cmi-intent-classifier) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0883
- Accuracy: {'accuracy': 0.9902386117136659}
- Precision: {'precision': 0.9903510498065617}
- F1: {'f1': 0.9902182723563171}
- Recall: {'recall': 0.9902386117136659}

## 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
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | Precision                         | F1                         | Recall                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------------:|:--------------------------:|:------------------------------:|
| No log        | 1.0   | 231  | 0.0989          | {'accuracy': 0.982646420824295}  | {'precision': 0.9830026219988417} | {'f1': 0.9826146119352713} | {'recall': 0.982646420824295}  |
| No log        | 2.0   | 462  | 0.0713          | {'accuracy': 0.9848156182212582} | {'precision': 0.9848586315054753} | {'f1': 0.9848051991118612} | {'recall': 0.9848156182212582} |
| 0.0556        | 3.0   | 693  | 0.0759          | {'accuracy': 0.9891540130151844} | {'precision': 0.9892928162619692} | {'f1': 0.9891645622565809} | {'recall': 0.9891540130151844} |
| 0.0556        | 4.0   | 924  | 0.0781          | {'accuracy': 0.9880694143167028} | {'precision': 0.9882940651710526} | {'f1': 0.9880558669679527} | {'recall': 0.9880694143167028} |
| 0.01          | 5.0   | 1155 | 0.0952          | {'accuracy': 0.9859002169197397} | {'precision': 0.9860278014567007} | {'f1': 0.9858894411052812} | {'recall': 0.9859002169197397} |
| 0.01          | 6.0   | 1386 | 0.0951          | {'accuracy': 0.9848156182212582} | {'precision': 0.9850183276136155} | {'f1': 0.9847935148784525} | {'recall': 0.9848156182212582} |
| 0.0052        | 7.0   | 1617 | 0.1083          | {'accuracy': 0.982646420824295}  | {'precision': 0.9832358024736132} | {'f1': 0.9826372058370486} | {'recall': 0.982646420824295}  |
| 0.0052        | 8.0   | 1848 | 0.0894          | {'accuracy': 0.9891540130151844} | {'precision': 0.9892617252983917} | {'f1': 0.9891180233703651} | {'recall': 0.9891540130151844} |
| 0.0015        | 9.0   | 2079 | 0.0829          | {'accuracy': 0.9913232104121475} | {'precision': 0.9914580258788241} | {'f1': 0.9913024714765813} | {'recall': 0.9913232104121475} |
| 0.0015        | 10.0  | 2310 | 0.0775          | {'accuracy': 0.9913232104121475} | {'precision': 0.9914039091193311} | {'f1': 0.9912930173649313} | {'recall': 0.9913232104121475} |
| 0.0015        | 11.0  | 2541 | 0.0857          | {'accuracy': 0.9891540130151844} | {'precision': 0.9892478213519429} | {'f1': 0.9891377567928298} | {'recall': 0.9891540130151844} |
| 0.0015        | 12.0  | 2772 | 0.0868          | {'accuracy': 0.9891540130151844} | {'precision': 0.9892478213519429} | {'f1': 0.9891377567928298} | {'recall': 0.9891540130151844} |
| 0.0001        | 13.0  | 3003 | 0.0873          | {'accuracy': 0.9902386117136659} | {'precision': 0.9903510498065617} | {'f1': 0.9902182723563171} | {'recall': 0.9902386117136659} |
| 0.0001        | 14.0  | 3234 | 0.0877          | {'accuracy': 0.9902386117136659} | {'precision': 0.9903510498065617} | {'f1': 0.9902182723563171} | {'recall': 0.9902386117136659} |
| 0.0001        | 15.0  | 3465 | 0.0881          | {'accuracy': 0.9902386117136659} | {'precision': 0.9903510498065617} | {'f1': 0.9902182723563171} | {'recall': 0.9902386117136659} |
| 0.0001        | 16.0  | 3696 | 0.0883          | {'accuracy': 0.9902386117136659} | {'precision': 0.9903510498065617} | {'f1': 0.9902182723563171} | {'recall': 0.9902386117136659} |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2