Model Card

README.md

@@ -4,20 +4,71 @@ tags:
 license: apache-2.0
 ---
 
-### opus-mt-it-en
+# opus-mt-it-en
 
-* source languages: it
-* target languages: en
-*  OPUS readme: [it-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/it-en/README.md)
+## Table of Contents
+- [Model Details](#model-details)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
 
-*  dataset: opus
-* model: transformer-align
-* pre-processing: normalization + SentencePiece
-* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.zip)
-* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.test.txt)
-* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.eval.txt)
 
-## Benchmarks
+## Model Details
+**Model Description:**
+- **Developed by:** [Language Technology Research Group at the University of Helsinki](https://blogs.helsinki.fi/language-technology/)
+- **Model Type:** transformer-align
+- **Language(s):**  
+  - Source Language: Italian
+  - Target Language: English 
+- **License:** apache-2.0
+- **Resources for more information:**
+  - [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
+
+## How to Get Started With the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-it-en")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-it-en")
+```
+
+## 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: [it-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/it-en/README.md)
+
+
+
+#### Training Data
+##### Preprocessing
+* **Pre-processing:** Normalization + SentencePiece
+
+* **Dataset:** [opus](https://github.com/Helsinki-NLP/Opus-MT)
+* **Download original weights:** [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.zip)
+
+* **Test set translations:** [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.test.txt)
+
+## Evaluation
+
+### Results
+
+* **Test set scores:** [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/it-en/opus-2019-12-18.eval.txt)
+
+#### Benchmarks
 
 | testset               | BLEU  | chr-F |
 |-----------------------|-------|-------|