Create README.md

README.md

@@ -0,0 +1,93 @@
+---
+language: 
+- zh
+- en
+
+tags:
+- translation
+
+license: cc-by-4.0
+---
+
+### zho-eng
+
+## Table of Contents
+- [Model Details](#model-details)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Citation Information](#citation-information)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+
+## Model Details
+- **Model Description:**
+- **Developed by:** Algmon
+- **Model Type:** Translation
+- **Language(s):**  
+  - Source Language: Chinese
+  - Target Language: English
+- **License:** CC-BY-4.0
+- **Resources for more information:**
+  - [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
+
+
+## Uses
+
+#### Direct Use
+
+This model can be used for translation and text-to-text generation.
+
+
+## Risks, Limitations, and Biases
+
+**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+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)).
+
+Further details about the dataset for this model can be found in the OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md)
+
+## Training
+
+#### System Information 
+* helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
+* transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
+* port_machine: brutasse
+* port_time: 2020-08-21-14:41
+* src_multilingual: False
+* tgt_multilingual: False
+
+#### Training Data
+##### Preprocessing
+* pre-processing: normalization + SentencePiece (spm32k,spm32k)
+* ref_len: 82826.0
+* dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT)
+* download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip)
+
+* test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt)
+
+
+## Evaluation
+
+#### Results
+
+* test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.eval.txt)
+
+* brevity_penalty: 0.948
+
+
+## Benchmarks
+
+| testset               | BLEU  | chr-F |
+|-----------------------|-------|-------|
+| Tatoeba-test.zho.eng 	| 36.1 	| 0.548 |
+
+## How to Get Started With the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
+```