File size: 4,033 Bytes
4b45a3c |
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 |
---
library_name: transformers
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- arrow
model-index:
- name: text-to-sparql-t5-base-2024-10-01_04-05
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. -->
# text-to-sparql-t5-base-2024-10-01_04-05
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1612
- Gen Len: 19.0
- Bertscorer-p: 0.6042
- Bertscorer-r: 0.1007
- Bertscorer-f1: 0.3406
- Sacrebleu-score: 6.3972
- Sacrebleu-precisions: [93.50202971813725, 87.89528553225993, 83.9093099978942, 81.08246812206387]
- Bleu-bp: 0.0740
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:|
| 0.1434 | 1.0 | 4772 | 0.1290 | 19.0 | 0.5779 | 0.0743 | 0.3142 | 5.8962 | [92.35991566894258, 84.39366674829903, 78.94400227401933, 75.86961452759951] | 0.0713 |
| 0.0942 | 2.0 | 9544 | 0.1177 | 19.0 | 0.5888 | 0.0849 | 0.3250 | 6.1087 | [92.5606800784706, 85.52426907082315, 80.69350019995765, 77.57006871168893] | 0.0728 |
| 0.0653 | 3.0 | 14316 | 0.1173 | 19.0 | 0.6046 | 0.1056 | 0.3434 | 6.3214 | [93.2540100046867, 86.96274167420529, 82.274102896671, 78.77417998317914] | 0.0742 |
| 0.0483 | 4.0 | 19088 | 0.1232 | 19.0 | 0.5986 | 0.0961 | 0.3355 | 6.2622 | [93.15494173500215, 86.84532601814729, 82.2615628114192, 79.1214879303522] | 0.0735 |
| 0.0334 | 5.0 | 23860 | 0.1311 | 19.0 | 0.6023 | 0.0994 | 0.3390 | 6.3073 | [93.43068494727854, 87.49234763885077, 83.1708833292281, 80.1232645304334] | 0.0734 |
| 0.0235 | 6.0 | 28632 | 0.1357 | 19.0 | 0.6001 | 0.0980 | 0.3372 | 6.3131 | [93.21137315406656, 87.16716210233382, 82.85332802379921, 79.83819964161484] | 0.0737 |
| 0.0168 | 7.0 | 33404 | 0.1473 | 19.0 | 0.6041 | 0.1033 | 0.3419 | 6.4057 | [93.29664975783108, 87.43513246633191, 83.24213326488467, 80.18603064651553] | 0.0746 |
| 0.0119 | 8.0 | 38176 | 0.1505 | 19.0 | 0.6012 | 0.0990 | 0.3382 | 6.3570 | [93.1113662456946, 87.19629610143632, 83.0426651081239, 80.06573325445343] | 0.0742 |
| 0.0088 | 9.0 | 42948 | 0.1542 | 19.0 | 0.6055 | 0.1041 | 0.3430 | 6.4203 | [93.41891452713682, 87.77185624336455, 83.69605828507379, 80.74261780654649] | 0.0744 |
| 0.0071 | 10.0 | 47720 | 0.1612 | 19.0 | 0.6042 | 0.1007 | 0.3406 | 6.3972 | [93.50202971813725, 87.89528553225993, 83.9093099978942, 81.08246812206387] | 0.0740 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
|