File size: 2,237 Bytes
7a8ce8f fad170e 7a8ce8f 0dd7817 7a8ce8f f26dbd7 7a8ce8f 0dd7817 7a8ce8f 0dd7817 7a8ce8f fad170e |
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 |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: custom-flan-t5-small-hallucination-classification
results: []
pipeline_tag: text-classification
---
<!-- 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. -->
# custom-flan-t5-small-hallucination-classification
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6832
- Precision: 0.7473
- Recall: 0.7470
- F1: 0.7393
- Accuracy: 0.7470
## Model description
Classification head of T5 changed. Layer normalization and GELU added.
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9007 | 0.4016 | 100 | 0.8154 | 0.6926 | 0.6596 | 0.6202 | 0.6596 |
| 0.7915 | 0.8032 | 200 | 0.7602 | 0.7181 | 0.6867 | 0.6585 | 0.6867 |
| 0.7089 | 1.2048 | 300 | 0.6769 | 0.7323 | 0.7329 | 0.7250 | 0.7329 |
| 0.6381 | 1.6064 | 400 | 0.6954 | 0.7338 | 0.7329 | 0.7240 | 0.7329 |
| 0.6528 | 2.0080 | 500 | 0.6510 | 0.7423 | 0.7410 | 0.7318 | 0.7410 |
| 0.5652 | 2.4096 | 600 | 0.6856 | 0.7340 | 0.7339 | 0.7303 | 0.7339 |
| 0.5446 | 2.8112 | 700 | 0.6832 | 0.7473 | 0.7470 | 0.7393 | 0.7470 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |