datasets:
- glue
- anli
model-index:
- name: e5-large-mnli-anli
results: []
pipeline_tag: zero-shot-classification
language:
- en
license: mit
e5-large-mnli-anli
This model is a fine-tuned version of intfloat/e5-large on the glue (mnli) and anli dataset.
Model description
Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
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/e5-large-mnli-anli")
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/e5-large-mnli-anli"
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 |
|---|---|---|---|---|---|
| e5-base-mnli | 0.840 | 0.839 | 0.231 | 0.285 | 0.309 |
| e5-base-v2-mnli | 0.844 | 0.838 | 0.253 | 0.288 | 0.301 |
| e5-large-mnli | 0.868 | 0.869 | 0.301 | 0.296 | 0.294 |
| e5-large-v2-mnli | 0.875 | 0.876 | 0.354 | 0.298 | 0.313 |
| e5-large-unsupervised-mnli | 0.865 | 0.867 | 0.314 | 0.285 | 0.303 |
| e5-large-mnli-anli | 0.843 | 0.848 | 0.646 | 0.484 | 0.458 |
| e5-large-unsupervised-mnli-anli | 0.836 | 0.842 | 0.634 | 0.481 | 0.478 |
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: 2
Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1