Instructions to use bsaurav/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bsaurav/results with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bsaurav/results") model = AutoModelForSeq2SeqLM.from_pretrained("bsaurav/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
results2
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: 4.0082
- Rouge1: 0.2
- Rouge2: 0.0
- Rougel: 0.2
- Rougelsum: 0.2
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: 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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 5 | 4.1587 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 10 | 3.9220 | 0.2 | 0.0 | 0.2 | 0.2 |
| No log | 3.0 | 15 | 3.9029 | 0.2 | 0.0 | 0.2 | 0.2 |
| No log | 4.0 | 20 | 3.9380 | 0.2 | 0.0 | 0.2 | 0.2 |
| No log | 5.0 | 25 | 3.9846 | 0.2 | 0.0 | 0.2 | 0.2 |
| No log | 6.0 | 30 | 4.0082 | 0.2 | 0.0 | 0.2 | 0.2 |
Framework versions
- Transformers 4.27.2
- Pytorch 2.1.1
- Datasets 2.11.0
- Tokenizers 0.13.3
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