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--- |
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datasets: |
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- glue |
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- anli |
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model-index: |
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- name: bge-large-en-mnli-anli |
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results: [] |
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pipeline_tag: zero-shot-classification |
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language: |
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- en |
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license: mit |
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--- |
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# bge-large-en-mnli-anli |
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This model is a fine-tuned version of [BAAI/bge-large-en](https://huggingface.co./BAAI/bge-large-en) on the glue and ANLI dataset. |
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## Model description |
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[RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder](https://arxiv.org/abs/2205.12035). |
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Shitao Xiao, Zheng Liu, Yingxia Shao, Zhao Cao, arXiv 2022 |
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## How to use the model |
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### With the zero-shot classification pipeline |
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The model can be loaded with the `zero-shot-classification` pipeline like so: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", |
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model="mjwong/bge-large-en-mnli-anli") |
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``` |
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You can then use this pipeline to classify sequences into any of the class names you specify. |
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```python |
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sequence_to_classify = "one day I will see the world" |
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candidate_labels = ['travel', 'cooking', 'dancing'] |
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classifier(sequence_to_classify, candidate_labels) |
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``` |
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If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently: |
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```python |
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candidate_labels = ['travel', 'cooking', 'dancing', 'exploration'] |
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classifier(sequence_to_classify, candidate_labels, multi_class=True) |
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``` |
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### With manual PyTorch |
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The model can also be applied on NLI tasks like so: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# device = "cuda:0" or "cpu" |
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
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model_name = "mjwong/bge-large-en-mnli-anli" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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premise = "But I thought you'd sworn off coffee." |
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hypothesis = "I thought that you vowed to drink more coffee." |
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input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") |
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output = model(input["input_ids"].to(device)) |
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prediction = torch.softmax(output["logits"][0], -1).tolist() |
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label_names = ["entailment", "neutral", "contradiction"] |
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prediction = {name: round(float(pred) * 100, 2) for pred, name in zip(prediction, label_names)} |
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print(prediction) |
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``` |
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### Eval results |
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The model was also evaluated using the dev sets for MultiNLI and test sets for ANLI. The metric used is accuracy. |
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|Datasets|mnli_dev_m|mnli_dev_mm|anli_test_r1|anli_test_r2|anli_test_r3| |
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| :---: | :---: | :---: | :---: | :---: | :---: | |
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|[bge-large-en-mnli-anli](https://huggingface.co./mjwong/bge-large-en-mnli-anli)|0.846|0.842|0.602|0.451|0.452| |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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