metadata

tags:
  - generated_from_trainer
datasets:
  - boolq
metrics:
  - accuracy
base_model: nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large
model-index:
  - name: MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: boolq
          type: boolq
          config: default
          split: validation
          args: default
        metrics:
          - type: accuracy
            value: 0.7379204892966361
            name: Accuracy

MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq

This model is a fine-tuned version of nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large on the boolq dataset. It achieves the following results on the evaluation set:

Loss: 0.5417
Accuracy: 0.7379

Inference Example

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("nfliu/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq")
tokenizer = AutoTokenizer.from_pretrained("nfliu/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large_boolq")

# Each example is a (question, context) pair.
examples = [
    ("Lake Tahoe is in California", "Lake Tahoe is a popular tourist spot in California."),
    ("Water is wet", "Contrary to popular belief, water is not wet.")
]

encoded_input = tokenizer(examples, padding=True, truncation=True, return_tensors="pt")

with torch.no_grad():
    model_output = model(**encoded_input)
    probabilities = torch.softmax(model_output.logits, dim=-1).cpu().tolist()

probability_no = [round(prob[0], 2) for prob in probabilities]
probability_yes = [round(prob[1], 2) for prob in probabilities]

for example, p_no, p_yes in zip(examples, probability_no, probability_yes):
    print(f"Question: {example[0]}")
    print(f"Context: {example[1]}")
    print(f"p(No | question, context): {p_no}")
    print(f"p(Yes | question, context): {p_yes}")
    print()

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: 1e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.85	250	0.6579	0.6190
0.6352	1.69	500	0.5907	0.6841
0.6352	2.54	750	0.5613	0.7196
0.535	3.39	1000	0.5444	0.7373
0.535	4.24	1250	0.5417	0.7379

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3