datasets:
- glue
model-index:
- name: contriever-msmarco-mnli
results: []
pipeline_tag: zero-shot-classification
language:
- en
license: mit
contriever-msmarco-mnli
This model is a fine-tuned version of facebook/contriever-msmarco on the glue dataset.
Model description
Unsupervised Dense Information Retrieval with Contrastive Learning. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave, arXiv 2021
How to use the model
With the zero-shot classification pipeline
The model can be loaded with the zero-shot-classification pipeline like so:
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="mjwong/contriever-msmarco-mnli")
You can then use this pipeline to classify sequences into any of the class names you specify.
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
If more than one candidate label can be correct, pass multi_class=True to calculate each class independently:
candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_class=True)
With manual PyTorch
The model can also be applied on NLI tasks like so:
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# device = "cuda:0" or "cpu"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "mjwong/contriever-msmarco-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "But I thought you'd sworn off coffee."
hypothesis = "I thought that you vowed to drink more coffee."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 2) for pred, name in zip(prediction, label_names)}
print(prediction)
Eval results
The model was evaluated using the dev sets for MultiNLI and test sets for ANLI. The metric used is accuracy.
| Datasets | mnli_dev_m | mnli_dev_mm | anli_test_r1 | anli_test_r2 | anli_test_r3 |
|---|---|---|---|---|---|
| contriever-mnli | 0.821 | 0.822 | 0.247 | 0.281 | 0.312 |
| contriever-msmarco-mnli | 0.820 | 0.819 | 0.244 | 0.296 | 0.306 |
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1