TFG
Collection
Datasets and models leveraged and developed during my final degree work (TFG). Info and code can be found at https://github.com/enriquesaou/tfg-lm-qa • 18 items • Updated • 2
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("enriquesaou/roberta-mrqa")
model = AutoModelForQuestionAnswering.from_pretrained("enriquesaou/roberta-mrqa")Quick Links
roberta-mrqa
This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6199
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: 2e-05
- train_batch_size: 20
- eval_batch_size: 20
- 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 |
|---|---|---|---|
| 1.7637 | 1.0 | 1399 | 1.5842 |
| 1.3627 | 2.0 | 2798 | 1.6059 |
| 1.1236 | 3.0 | 4197 | 1.6199 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for enriquesaou/roberta-mrqa
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="enriquesaou/roberta-mrqa")