Instructions to use mohsin-aslam/text2sql-finetuned-60M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohsin-aslam/text2sql-finetuned-60M with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mohsin-aslam/text2sql-finetuned-60M") model = AutoModelForSeq2SeqLM.from_pretrained("mohsin-aslam/text2sql-finetuned-60M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,161 Bytes
eb4fe62 10e98cf eb4fe62 9353226 10e98cf eb4fe62 10e98cf eb4fe62 3a6cbe3 10e98cf eb4fe62 10e98cf eb4fe62 10e98cf | 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 | ---
license: apache-2.0
base_model: cssupport/t5-small-awesome-text-to-sql
tags:
- generated_from_trainer
model-index:
- name: text2sql-finetuned-60M
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. -->
# text2sql-finetuned-60M
This model is a fine-tuned version of [cssupport/t5-small-awesome-text-to-sql](https://huggingface.co/cssupport/t5-small-awesome-text-to-sql) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0054
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 2100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3586 | 0.29 | 150 | 0.1971 |
| 0.1748 | 0.57 | 300 | 0.0731 |
| 0.1453 | 0.86 | 450 | 0.0360 |
| 0.0947 | 1.15 | 600 | 0.0215 |
| 0.0816 | 1.44 | 750 | 0.0145 |
| 0.0635 | 1.72 | 900 | 0.0106 |
| 0.0464 | 2.01 | 1050 | 0.0085 |
| 0.0472 | 2.3 | 1200 | 0.0073 |
| 0.0414 | 2.59 | 1350 | 0.0065 |
| 0.0376 | 2.87 | 1500 | 0.0061 |
| 0.0342 | 3.16 | 1650 | 0.0057 |
| 0.0363 | 3.45 | 1800 | 0.0055 |
| 0.0389 | 3.74 | 1950 | 0.0055 |
| 0.0308 | 4.02 | 2100 | 0.0054 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|