Instructions to use learn3r/longt5_xl_gov_bp_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use learn3r/longt5_xl_gov_bp_5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("learn3r/longt5_xl_gov_bp_5") model = AutoModelForSeq2SeqLM.from_pretrained("learn3r/longt5_xl_gov_bp_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: google/long-t5-tglobal-xl
tags:
- generated_from_trainer
datasets:
- learn3r/gov_report_bp
model-index:
- name: longt5_xl_gov_bp_5
results: []
longt5_xl_gov_bp_5
This model is a fine-tuned version of google/long-t5-tglobal-xl on the learn3r/gov_report_bp dataset. It achieves the following results on the evaluation set:
- Loss: 1.0956
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: 0.001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2876 | 1.0 | 136 | 1.1481 |
| 1.1337 | 1.99 | 272 | 1.1011 |
| 1.0035 | 3.0 | 409 | 1.0956 |
| 0.8616 | 4.0 | 545 | 1.1049 |
| 0.7515 | 4.99 | 680 | 1.1395 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1