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---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
- meteor
datasets:
- natural_questions
model-index:
- name: mt5-small
results:
- task:
type: Question answering from context # Required. Example: automatic-speech-recognition
name: Question answering # Optional. Example: Speech Recognition
dataset:
type: natural-questions # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Adapted Natural Questions # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: bleu
value: 34.1596
name: BLEU
verified: true
- type: rouge
value: 44.4366
name: ROUGE1
verified: true
- type: rouge
value: 38.8202
name: ROUGE2
verified: true
- type: rouge
value: 43.113
name: ROUGEl
verified: true
- type: rouge
value: 43.1423
name: ROUGElsum
verified: true
- type: meteor
value: 0.4049
name: METEOR
verified: true
---
# mt5-small_test_45
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an enhanced version of the Natural Questions dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7291
- Rouge1: 44.4366
- Rouge2: 38.8202
- Rougel: 43.113
- Rougelsum: 43.1423
- Bleu: 34.1596
- Gen Len: 12.6724
- Meteor: 0.4049
- True negatives: 69.7281
- False negatives: 10.4037
- Cosine Sim: 0.763
## Model description
This model is fine-tuned for long-form, closed-domain question answering - question-answering from context. It uses a heavily refined version of Google's Natural Questions dataset.
Answers to the questions were rewritten using OpenAI's GPT-3.5 Turbo model.
Please see [the following repo](https://github.com/pointonjoel/MSc-Diss) for all code and adaptations.
## Intended uses & limitations
The model requires questions to be submitted using the following format using the input message:
\[CONTEXT\] <\s> \[QUESTION\]
It is trained to respond appropriately when a question cannot be answered using the provided context.
It can give false negatives and false positives on occasion (see Training Results), and all answers must be checked appropriately.
## 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: 16
- eval_batch_size: 16
- seed: 9
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- weight_decay = 0.007
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | Meteor | True negatives | False negatives | Cosine Sim |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:--------------:|:---------------:|:----------:|
| 2.5724 | 1.0 | 175 | 0.9876 | 18.7781 | 15.6002 | 18.22 | 18.2686 | 7.6676 | 7.7661 | 0.1628 | 72.8701 | 56.677 | 0.4003 |
| 1.1469 | 1.99 | 350 | 0.8580 | 36.8209 | 31.2514 | 35.5008 | 35.5462 | 25.7137 | 12.0014 | 0.3311 | 62.8399 | 20.3934 | 0.6645 |
| 0.9468 | 2.99 | 525 | 0.7997 | 40.4128 | 34.716 | 39.0867 | 39.0972 | 29.3028 | 12.4287 | 0.3656 | 63.4441 | 15.295 | 0.7114 |
| 0.8129 | 3.98 | 700 | 0.7733 | 42.6764 | 36.7266 | 41.2465 | 41.2833 | 32.0644 | 12.9002 | 0.3871 | 62.1752 | 11.413 | 0.7425 |
| 0.7228 | 4.98 | 875 | 0.7483 | 42.9082 | 36.957 | 41.482 | 41.5233 | 32.4942 | 12.8866 | 0.3906 | 63.3233 | 11.5166 | 0.747 |
| 0.6493 | 5.97 | 1050 | 0.7293 | 40.3205 | 34.9632 | 39.1111 | 39.1168 | 28.8249 | 11.6867 | 0.3674 | 73.8973 | 17.9865 | 0.7068 |
| 0.5883 | 6.97 | 1225 | 0.7172 | 42.7342 | 37.0855 | 41.4069 | 41.424 | 32.1296 | 12.48 | 0.3887 | 70.0302 | 12.7847 | 0.7392 |
| 0.5409 | 7.96 | 1400 | 0.7387 | 44.6657 | 38.8426 | 43.3276 | 43.3496 | 34.4773 | 12.9395 | 0.4084 | 66.3444 | 9.5238 | 0.7658 |
| 0.5035 | 8.96 | 1575 | 0.7330 | 43.4925 | 38.0013 | 42.2697 | 42.2372 | 32.6131 | 12.2789 | 0.3979 | 72.6284 | 12.8364 | 0.7451 |
| 0.4652 | 9.95 | 1750 | 0.7291 | 44.4366 | 38.8202 | 43.113 | 43.1423 | 34.1596 | 12.6724 | 0.4049 | 69.7281 | 10.4037 | 0.763 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 |