File size: 2,473 Bytes
ff0a9d5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotions-dataset-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.937
- name: F1
type: f1
value: 0.9369365562537507
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotions-dataset-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2689
- Accuracy: 0.937
- F1: 0.9369
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6095 | 1.0 | 500 | 0.2145 | 0.9245 | 0.9250 |
| 0.1686 | 2.0 | 1000 | 0.1670 | 0.933 | 0.9316 |
| 0.1164 | 3.0 | 1500 | 0.1631 | 0.939 | 0.9394 |
| 0.0924 | 4.0 | 2000 | 0.1829 | 0.938 | 0.9366 |
| 0.072 | 5.0 | 2500 | 0.1929 | 0.9355 | 0.9354 |
| 0.0545 | 6.0 | 3000 | 0.2117 | 0.9355 | 0.9357 |
| 0.0432 | 7.0 | 3500 | 0.2222 | 0.934 | 0.9340 |
| 0.0298 | 8.0 | 4000 | 0.2553 | 0.939 | 0.9385 |
| 0.0248 | 9.0 | 4500 | 0.2667 | 0.936 | 0.9358 |
| 0.0199 | 10.0 | 5000 | 0.2689 | 0.937 | 0.9369 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|