---
library_name: transformers
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- generated_from_trainer
- extractive_QA
model-index:
- name: bert-mini-squadv2
  results: []
datasets:
- hf-tuner/squad_v2.0.1
language:
- en
metrics:
- exact_match
pipeline_tag: question-answering
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-mini-squadv2

This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on 
[hf-tuner/squad_v2.0.1](https://huggingface.co/datasets/hf-tuner/squad_v2.0.1) dataset.

It achieves the following results on the evaluation set:
- Loss: 1.4653
- Exact Match Accuracy: 62.95%

## Evaluation Notes

#### Issues with Exact Match Evaluation
Several correct predictions were incorrectly marked as false negatives due to strict exact-match criteria being sensitive to minor differences in tokenization, formatting, or span boundaries:

- Predicted: `schrodinger equation` → Rejected (expected: `schrödinger equation`)
- Predicted: `feynman diagrams` → Rejected (expected: `feynman`)
- Predicted: `electromagnetic force` → Rejected (expected: `electromagnetic`)
- Predicted: `45 000 pounds` → Rejected (expected: `45000 pounds`)

#### Overall Performance
- Exact-match accuracy: **>63%**
- The model frequently generates high-quality and semantically correct answer spans even when exact-match evaluation penalizes them.
- Primary limitation: performance drops on questions requiring deep domain-specific knowledge, largely attributable to the model's relatively small size and limited parameter capacity.

#### Recommendations for Best Results
- Use clear, straightforward phrasing in queries to maximize extraction accuracy.

## Model description

MiniLMv1-L12-H384-uncased: 12-layer, 384-hidden, 12-heads, 33M parameters, 2.7x faster than BERT-Base

#### Direct Use
- Extractive Question Answering: Given a passage and a question, the model extracts the most likely span of text that answers the question.
- Handles unanswerable questions by predicting "no answer" when appropriate.

#### Downstream Use
Can be integrated into chatbots, virtual assistants, or search systems that require question answering over text.

#### Out-of-Scope Use
- Generative question answering (the model cannot generate new answers).
- Non-English tasks (the model was trained only on English data).
- Open-Domain QA across large corpora — works best when the context passage is provided.

## How to use

```python
import torch
from transformers import BertForQuestionAnswering, AutoTokenizer

model_id='hf-tuner/bert-mini-squadv2'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained(model_id)
bert_qa = BertForQuestionAnswering.from_pretrained(model_id).to(device)
bert_qa = bert_qa.half()

def get_answers(ctxq):
  inputs = tokenizer(ctxq, padding=True, return_tensors='pt')
  for k,v in inputs.items():
    inputs[k] = v.to(device)

  with torch.no_grad():
    outputs = bert_qa(**inputs)

  start_idxs = outputs.start_logits.argmax(dim=-1)
  end_idxs = outputs.end_logits.argmax(dim=-1)

  predictions = []
  for i, (start_idx, end_idx) in enumerate(zip(start_idxs, end_idxs)):
    if start_idx == end_idx:
      predictions.append("<no_answer>")
    else:
      predict_answer_tokens = inputs['input_ids'][i, start_idx : end_idx]
      pred_answer = tokenizer.decode(predict_answer_tokens)
      predictions.append(pred_answer)
  return predictions


context = """In Q3 2024, xAI raised $6 billion in a Series C round led by Valor Equity Partners and Andreessen Horowitz, with participation from Sequoia Capital, Fidelity, and Saudi Arabia’s Kingdom Holding Company, bringing its post-money valuation to $50 billion.
"""
question_1 = "Which two investors co-led xAI’s $6 billion Series C round announced in Q3 2024?"
question_2 = "On what exact date in Q3 2024 was xAI’s $6 billion Series C funding round officially closed?"

get_answers([
    [context, question_1],
    [context, question_2],
])

>>> ['valor equity partners and andreessen horowitz', '<no_answer>']

```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3678        | 1.0   | 8134  | 1.4974          |
| 1.1809        | 2.0   | 16268 | 1.4653          |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1