File size: 2,025 Bytes
f32f516 a5dd5af f32f516 a5dd5af f32f516 |
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 |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: flan-t5-small-qclassifier_new_median-droprob_0.2-smooth_0.1-lr_1e-5-dcy_0.0001
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. -->
# flan-t5-small-qclassifier_new_median-droprob_0.2-smooth_0.1-lr_1e-5-dcy_0.0001
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6502
- Precision: 0.6500
- Recall: 1.0
- F1: 0.7879
## 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: 1e-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: 15
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.6701 | 1.0 | 193 | 0.6502 | 0.6500 | 1.0 | 0.7879 |
| 0.6504 | 2.0 | 386 | 0.6435 | 0.6500 | 1.0 | 0.7879 |
| 0.643 | 3.0 | 579 | 0.6382 | 0.6494 | 0.9970 | 0.7865 |
| 0.6379 | 4.0 | 772 | 0.6351 | 0.6504 | 0.9831 | 0.7829 |
| 0.632 | 5.0 | 965 | 0.6309 | 0.6653 | 0.9511 | 0.7829 |
| 0.6281 | 6.0 | 1158 | 0.6299 | 0.6695 | 0.9472 | 0.7845 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|