Create README.md

README.md

@@ -1,3 +1,76 @@
 ---
+datasets:
+- glue
+model-index:
+- name: e5-large-mnli
+  results: []
+pipeline_tag: zero-shot-classification
+language:
+- en
 license: mit
 ---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# e5-large-mnli
+
+This model is a fine-tuned version of [intfloat/e5-large](https://huggingface.co/intfloat/e5-large) 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-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': [0.9494319558143616, 0.044598229229450226, 0.00596982054412365]}
+```
+
+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': ['exploration', 'travel', 'dancing', 'cooking'],
+# 'scores': [0.9918234944343567,
+#  0.9867327213287354,
+#  0.40335655212402344,
+#  0.0020157278049737215]}
+```
+
+### 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