README.md

---
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

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](https://ai.google.com/research/NaturalQuestions/).

Answers to the questions were rewritten using [OpenAI's GPT-3.5 Turbo model](https://platform.openai.com/docs/models).

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

The model is trained using the Natural Questions dataset, with answers that have been refined using GPT-3.5 Turbo. It is evaluated using a number of metrics including BLEU, ROUGE, METEOR, and cosine similarity. 

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "psxjp5/mt5-small"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Generate text
context = "Once upon a time"
question = "What is time"
input_ids = tokenizer(context, question, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_new_tokens=150)

print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## 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
- dropout: 0.4

### 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         | 0.66
|
| 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         | 0.7```1     |
| 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