Instructions to use ativilambit/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ativilambit/results with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ativilambit/results") model = AutoModelForSeq2SeqLM.from_pretrained("ativilambit/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: results
results: []
results
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0912
- Rouge1: 0.2091
- Rouge2: 0.1205
- Rougel: 0.1845
- Rougelsum: 0.1965
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: 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 | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 17 | 1.3457 | 0.2023 | 0.0986 | 0.1645 | 0.1834 |
| No log | 2.0 | 34 | 1.1429 | 0.2047 | 0.1129 | 0.1777 | 0.1930 |
| No log | 3.0 | 51 | 1.0912 | 0.2091 | 0.1205 | 0.1845 | 0.1965 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3