model documentation (#1)

- model documentation (c465610e250c284a36a9cb4cdc8b4e3072f1848f)


Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>

README.md

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+---
+tags:
+- feature-extraction
+- bert
+---
+
+# Model Card for unsup-simcse-bert-large-uncased 
+ 
+# Model Details
+ 
+## Model Description
+ 
+More information needed
+ 
+- **Developed by:** Princeton NLP group
+- **Shared by [Optional]:** Princeton NLP group
+
+- **Model type:** Feature Extraction
+- **Language(s) (NLP):** More information needed
+- **License:** More information needed
+- **Parent Model:** BERT
+- **Resources for more information:**
+ 	- [GitHub Repo](https://github.com/princeton-nlp/SimCSE)
+ 	 - [Associated Paper](https://arxiv.org/abs/2104.08821)
+
+
+# Uses
+ 
+
+## Direct Use
+This model can be used for the task of feature extraction. 
+ 
+## Downstream Use [Optional]
+ 
+More information needed.
+ 
+## Out-of-Scope Use
+ 
+The model should not be used to intentionally create hostile or alienating environments for people. 
+ 
+# Bias, Risks, and Limitations
+ 
+ 
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+
+
+
+## Recommendations
+ 
+ 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+# Training Details
+ 
+## Training Data
+ 
+  The model craters note in the [associatedGithub Repository](https://github.com/princeton-nlp/SimCSE/blob/main/README.md):
+> We train unsupervised SimCSE on 106 randomly sampled sentences from English Wikipedia, and train supervised SimCSE on the combination of MNLI and SNLI datasets (314k).
+ 
+ 
+ 
+## Training Procedure
+
+ 
+### Preprocessing
+ 
+More information needed 
+
+
+ 
+### Speeds, Sizes, Times
+ 
+ 
+**Hyperparameters**
+The model craters note in the [associated GitHub Repo](https://github.com/princeton-nlp/SimCSE) : 
+ 
+|               | Unsup. BERT | Sup.      |
+|:--------------|:-----------:|:---------:|
+| Batch size    | 64          | 512       |
+| Learning rate (large) | 1e-5 | 1e-5 |
+ 
+
+
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+ The model craters note in the [associated paper](https://arxiv.org/pdf/2104.08821.pdf):
+> Our evaluation code for sentence embeddings is based on a modified version of [SentEval](https://github.com/facebookresearch/SentEval). It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. 
+ 
+> For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation. See [associated paper](https://arxiv.org/pdf/2104.08821.pdf) (Appendix B) for evaluation details.
+
+
+ 
+### Factors
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+ 
+## Results 
+ 
+More information needed
+
+ 
+# Model Examination
+ 
+ The model craters note in the [associated paper](https://arxiv.org/pdf/2104.08821.pdf):
+ 
+>**Uniformity and alignment.**
+ We also observe that (1) though pre-trained embeddings have good alignment, their uniformity is poor (i.e., the embeddings are highly anisotropic); (2) post-processing methods like BERT-flow and BERT-whitening greatly improve uniformity but also suffer a degeneration in alignment; (3) unsupervised SimCSE effectively improves uniformity of pre-trained embeddings whereas keeping a good alignment;(4) incorporating supervised data in SimCSE further amends alignment.
+ 
+
+
+ 
+# Environmental Impact
+ 
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+ 
+- **Hardware Type:** Nvidia 3090 GPUs with CUDA 11
+- **Hours used:** More information needed
+- **Cloud Provider:** More information needed
+- **Compute Region:** More information needed
+- **Carbon Emitted:** More information needed
+ 
+# Technical Specifications [optional]
+ 
+## Model Architecture and Objective
+ 
+More information needed
+ 
+## Compute Infrastructure
+ 
+More information needed
+ 
+### Hardware
+ 
+ 
+More information needed
+ 
+### Software
+ 
+More information needed.
+ 
+# Citation
+
+ 
+**BibTeX:**
+ 
+ 
+```bibtex
+@inproceedings{gao2021simcse,
+   title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
+   author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
+   booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
+   year={2021}
+}
+```
+ 
+ 
+ 
+ 
+# Glossary [optional]
+ 
+More information needed
+
+# More Information [optional]
+More information needed 
+
+ 
+# Model Card Authors [optional]
+ 
+Princeton NLP group in collaboration with Ezi Ozoani and the Hugging Face team.
+
+# Model Card Contact
+ 
+If you have any questions related to the code or the paper, feel free to email Tianyu (`tianyug@cs.princeton.edu`) and Xingcheng (`yxc18@mails.tsinghua.edu.cn`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!
+ 
+# How to Get Started with the Model
+ 
+Use the code below to get started with the model.
+ 
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/unsup-simcse-bert-large-uncased")
+
+model = AutoModel.from_pretrained("princeton-nlp/unsup-simcse-bert-large-uncased")
+ ```
+</details>
+
+