need_divert need_educate need_inspire need_perspective need_trend need_update
---
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: 1.0600
- Accuracy: 0.8479
- F1: 0.8319
- Precision: 0.8315
- Recall: 0.8479

## 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: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 98   | 1.1222          | 0.6263   | 0.5185 | 0.5076    | 0.6263 |
| No log        | 2.0   | 196  | 1.0066          | 0.7216   | 0.6436 | 0.5899    | 0.7216 |
| No log        | 3.0   | 294  | 0.8540          | 0.7577   | 0.7037 | 0.6760    | 0.7577 |
| No log        | 4.0   | 392  | 0.8621          | 0.7603   | 0.6998 | 0.6568    | 0.7603 |
| No log        | 5.0   | 490  | 0.8062          | 0.7887   | 0.7500 | 0.7449    | 0.7887 |
| 0.91          | 6.0   | 588  | 0.7465          | 0.8041   | 0.7660 | 0.7636    | 0.8041 |
| 0.91          | 7.0   | 686  | 0.6324          | 0.8247   | 0.8163 | 0.8187    | 0.8247 |
| 0.91          | 8.0   | 784  | 0.7333          | 0.7964   | 0.7703 | 0.7740    | 0.7964 |
| 0.91          | 9.0   | 882  | 0.6590          | 0.8325   | 0.8208 | 0.8106    | 0.8325 |
| 0.91          | 10.0  | 980  | 0.9854          | 0.8196   | 0.7890 | 0.7920    | 0.8196 |
| 0.4246        | 11.0  | 1078 | 0.7023          | 0.8247   | 0.8054 | 0.8138    | 0.8247 |
| 0.4246        | 12.0  | 1176 | 0.8995          | 0.8325   | 0.8120 | 0.8068    | 0.8325 |
| 0.4246        | 13.0  | 1274 | 0.8589          | 0.8299   | 0.8145 | 0.8058    | 0.8299 |
| 0.4246        | 14.0  | 1372 | 0.9859          | 0.8376   | 0.8151 | 0.8123    | 0.8376 |
| 0.4246        | 15.0  | 1470 | 0.8452          | 0.8402   | 0.8318 | 0.8341    | 0.8402 |
| 0.1637        | 16.0  | 1568 | 1.1156          | 0.8351   | 0.8157 | 0.8196    | 0.8351 |
| 0.1637        | 17.0  | 1666 | 1.1514          | 0.8325   | 0.8122 | 0.8218    | 0.8325 |
| 0.1637        | 18.0  | 1764 | 1.0092          | 0.8428   | 0.8266 | 0.8320    | 0.8428 |
| 0.1637        | 19.0  | 1862 | 1.0368          | 0.8351   | 0.8229 | 0.8287    | 0.8351 |
| 0.1637        | 20.0  | 1960 | 1.0600          | 0.8479   | 0.8319 | 0.8315    | 0.8479 |
| 0.0391        | 21.0  | 2058 | 1.1046          | 0.8428   | 0.8293 | 0.8269    | 0.8428 |
| 0.0391        | 22.0  | 2156 | 1.1178          | 0.8454   | 0.8262 | 0.8280    | 0.8454 |
| 0.0391        | 23.0  | 2254 | 1.1103          | 0.8428   | 0.8268 | 0.8295    | 0.8428 |
| 0.0391        | 24.0  | 2352 | 1.1179          | 0.8428   | 0.8274 | 0.8313    | 0.8428 |
| 0.0391        | 25.0  | 2450 | 1.1134          | 0.8402   | 0.8233 | 0.8254    | 0.8402 |


### Framework versions

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