---
license: cc-by-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: nb-bert-base-user-needs
  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. -->

# nb-bert-base-user-needs

This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6468
- Accuracy: 0.8582
- F1: 0.8388
- Precision: 0.8295
- Recall: 0.8582

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 98   | 1.2122          | 0.6005   | 0.4506 | 0.3606    | 0.6005 |
| No log        | 2.0   | 196  | 0.9735          | 0.7113   | 0.6231 | 0.5549    | 0.7113 |
| No log        | 3.0   | 294  | 0.7894          | 0.7655   | 0.6996 | 0.7399    | 0.7655 |
| No log        | 4.0   | 392  | 0.9499          | 0.6933   | 0.6584 | 0.6617    | 0.6933 |
| No log        | 5.0   | 490  | 0.7529          | 0.7784   | 0.7217 | 0.7107    | 0.7784 |
| 0.9006        | 6.0   | 588  | 0.7510          | 0.7964   | 0.7491 | 0.7370    | 0.7964 |
| 0.9006        | 7.0   | 686  | 0.5963          | 0.8273   | 0.8044 | 0.7960    | 0.8273 |
| 0.9006        | 8.0   | 784  | 0.6918          | 0.8351   | 0.8071 | 0.8096    | 0.8351 |
| 0.9006        | 9.0   | 882  | 0.7391          | 0.8273   | 0.8017 | 0.8042    | 0.8273 |
| 0.9006        | 10.0  | 980  | 0.6468          | 0.8582   | 0.8388 | 0.8295    | 0.8582 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1