---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
datasets:
- mtc/span_absinth_with_articles_german_faithfulness_detection_dataset
model-index:
- name: google-flan-t5-large_MAX-CONTEXT-LEN-1024_MAX-GEN-LEN-256_span_absinth_faithfulness_multi_label_classification_vertical-base-2024-07-15
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/background-tool/span_absinth_evaluation/runs/ogtrt5rw)
# google-flan-t5-large_MAX-CONTEXT-LEN-1024_MAX-GEN-LEN-256_span_absinth_faithfulness_multi_label_classification_vertical-base-2024-07-15

This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the mtc/span_absinth_with_articles_german_faithfulness_detection_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1019

## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.461         | 0.1534 | 100  | 0.2231          |
| 0.1298        | 0.3067 | 200  | 0.1552          |
| 0.1306        | 0.4601 | 300  | 0.1427          |
| 0.1075        | 0.6135 | 400  | 0.1253          |
| 0.0819        | 0.7669 | 500  | 0.1211          |
| 0.0991        | 0.9202 | 600  | 0.1101          |
| 0.0921        | 1.0736 | 700  | 0.1100          |
| 0.0692        | 1.2270 | 800  | 0.1066          |
| 0.0557        | 1.3804 | 900  | 0.1091          |
| 0.0546        | 1.5337 | 1000 | 0.1118          |
| 0.0754        | 1.6871 | 1100 | 0.1053          |
| 0.0554        | 1.8405 | 1200 | 0.1047          |
| 0.0585        | 1.9939 | 1300 | 0.1054          |
| 0.058         | 2.1472 | 1400 | 0.1035          |
| 0.0643        | 2.3006 | 1500 | 0.1025          |
| 0.046         | 2.4540 | 1600 | 0.1006          |
| 0.041         | 2.6074 | 1700 | 0.1025          |
| 0.0431        | 2.7607 | 1800 | 0.1023          |
| 0.0367        | 2.9141 | 1900 | 0.1022          |


### Framework versions

- Transformers 4.42.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1