File size: 1,958 Bytes
e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 e9b132b fa5cbd9 |
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 |
---
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: minilm_finetuned_emotions
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: F1
type: f1
value: 0.9103389601463553
---
<!-- 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. -->
# minilm_finetuned_emotions
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4118
- F1: 0.9103
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3758 | 1.0 | 250 | 1.0211 | 0.5910 |
| 0.885 | 2.0 | 500 | 0.7133 | 0.7977 |
| 0.6248 | 3.0 | 750 | 0.5321 | 0.8840 |
| 0.4874 | 4.0 | 1000 | 0.4416 | 0.9013 |
| 0.4193 | 5.0 | 1250 | 0.4118 | 0.9103 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|