Update README.md

README.md

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 ---
+datasets:
+- glue
+model-index:
+- name: e5-large-v2-mnli
+  results: []
+pipeline_tag: zero-shot-classification
+language:
+- en
 license: mit
 ---
+
+# e5-large-v2-mnli
+
+This model is a fine-tuned version of [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) on the glue dataset.
+
+## Model description
+
+[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
+Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
+
+## How to use the model
+
+The model can be loaded with the `zero-shot-classification` pipeline like so:
+
+```python
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification",
+                      model="mjwong/e5-large-v2-mnli")
+```
+
+You can then use this pipeline to classify sequences into any of the class names you specify.
+
+```python
+sequence_to_classify = "one day I will see the world"
+candidate_labels = ['travel', 'cooking', 'dancing']
+classifier(sequence_to_classify, candidate_labels)
+#{'sequence': 'one day I will see the world',
+# 'labels': ['travel', 'dancing', 'cooking'],
+# 'scores': [0.7971774339675903, 0.12801812589168549, 0.07480447739362717]}
+```
+
+If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
+
+```python
+candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
+classifier(sequence_to_classify, candidate_labels, multi_class=True)
+#{'sequence': 'one day I will see the world',
+# 'labels': ['travel', 'exploration', 'dancing', 'cooking'],
+# 'scores': [0.8738091588020325,
+#  0.8598912954330444,
+#  0.12812669575214386,
+#  0.01454145833849907]}
+```
+
+### 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](https://huggingface.co/mjwong/e5-base-mnli)|0.840|0.839|0.231|0.285|0.309|
+|[e5-base-v2-mnli](https://huggingface.co/mjwong/e5-base-v2-mnli)|0.844|0.838|0.253|0.288|0.301|
+|[e5-large-mnli](https://huggingface.co/mjwong/e5-large-mnli)|0.868|0.869|0.301|0.296|0.294|
+|[e5-large-v2-mnli](https://huggingface.co/mjwong/e5-large-v2-mnli)|0.875|0.876|0.354|0.298|0.313|
+|[e5-large-unsupervised-mnli](https://huggingface.co/mjwong/e5-large-unsupervised-mnli)|0.865|0.867|0.314|0.285|0.303|
+|[e5-large-mnli-anli](https://huggingface.co/mjwong/e5-large-mnli-anli)|0.843|0.848|0.646|0.484|0.458|
+|[e5-large-unsupervised-mnli-anli](https://huggingface.co/mjwong/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