Add model card.

README.md

@@ -2,22 +2,72 @@
 language:
 - en
 - cy
-license: apache-2.0
 pipeline_tag: translation
 tags:
 - translation
 - marian
 metrics:
-- type: bleu
-  value: 54.16
-- type: cer
-  value: 0.31
-- type: wer
-  value: 0.47
-- type: wil
-  value: 0.67
-- type: wip
-  value: 0.33
-- type: chrf
-  value: 69.03
+- bleu
+- cer
+- wer
+- wil
+- wip
+- chrf
+license: apache-2.0
+model-index:
+- name: "mt-dspec-health-en-cy"
+  results:
+  - task:
+      name: Translation
+      type: translation
+    metrics:
+    - name: SacreBLEU
+      type: bleu
+      value: 54.16
+    - name: CER
+      type: cer
+      value: 0.31
+    - name: WER
+      type: wer
+      value: 0.47
+    - name: WIL
+      type: wil
+      value: 0.67
+    - name: WIP
+      type: wip
+      value: 0.33
+    - name: SacreBLEU CHRF
+      type: chrf
+      value: 69.03
 ---
+# mt-dspec-health-en-cy
+A language translation model for translating between English and Welsh, specialised to the specific domain of Health and care.
+
+This model was trained using custom DVC pipeline employing [Marian NMT](https://marian-nmt.github.io/), 
+the datasets prepared were generated from the following sources:
+ - [UK Goverment Legislation data](https://www.legislation.gov.uk)
+ - [OPUS-cy-en](https://opus.nlpl.eu/)
+ - [Cofnod Y Cynulliad](https://record.assembly.wales/)
+ - [Cofion Techiaith Cymru](https://cofion.techiaith.cymru)
+
+The data was split into train, validation and tests sets, the test set containing health-spefic segemnts from TMX files
+selected at random from the [Cofion Techiaith Cymru](https://cofion.techiaith.cymru) website, which have been pre-classified as pertaining to the specific domain.
+Having extracted the test set, the aggregation of remaining data was then split into 10 training and valdiation sets, and fed into 10 marain training sessions.
+
+## Evaluation
+
+Evalatuaion was done using the python libraries [SacreBLEU](https://github.com/mjpost/sacrebleu) and [torchmetrics](https://torchmetrics.readthedocs.io/en/stable/).
+
+## Usage
+
+The mt-dspec-health-en-cy model can be used for inference directly as follows:
+
+```python
+import trnasformers
+model_id = "techiaith/mt-spec-health-en-cy"
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
+model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_id)
+translate = transformers.pipeline("translation", model=model, tokenizer=tokenizer)
+translated = translate("The doctor had many patients to attend to this morning.")
+print(translated["translation_text"])
+```