cometinho model

README.md

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 ---
+pipeline_tag: translation
+language:
+  - multilingual
+  - af
+  - am
+  - ar
+  - as
+  - az
+  - be
+  - bg
+  - bn
+  - br
+  - bs
+  - ca
+  - cs
+  - cy
+  - da
+  - de
+  - el
+  - en
+  - eo
+  - es
+  - et
+  - eu
+  - fa
+  - fi
+  - fr
+  - fy
+  - ga
+  - gd
+  - gl
+  - gu
+  - ha
+  - he
+  - hi
+  - hr
+  - hu
+  - hy
+  - id
+  - is
+  - it
+  - ja
+  - jv
+  - ka
+  - kk
+  - km
+  - kn
+  - ko
+  - ku
+  - ky
+  - la
+  - lo
+  - lt
+  - lv
+  - mg
+  - mk
+  - ml
+  - mn
+  - mr
+  - ms
+  - my
+  - ne
+  - nl
+  - 'no'
+  - om
+  - or
+  - pa
+  - pl
+  - ps
+  - pt
+  - ro
+  - ru
+  - sa
+  - sd
+  - si
+  - sk
+  - sl
+  - so
+  - sq
+  - sr
+  - su
+  - sv
+  - sw
+  - ta
+  - te
+  - th
+  - tl
+  - tr
+  - ug
+  - uk
+  - ur
+  - uz
+  - vi
+  - xh
+  - yi
+  - zh
 license: apache-2.0
 ---
+
+This is a distilled [COMET](https://github.com/Unbabel/COMET) model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference.
+
+# Paper
+
+[Searching for Cometinho: The Little Metric That Could](https://aclanthology.org/2022.eamt-1.9/)
+
+# License
+
+Apache-2.0
+
+# Usage (unbabel-comet)
+
+Using this model requires unbabel-comet to be installed:
+
+```bash
+pip install --upgrade pip  # ensures that pip is current 
+pip install unbabel-comet
+```
+
+Then you can use it through comet CLI:
+
+```bash
+comet-score -s {source-inputs}.txt -t {translation-outputs}.txt -r {references}.txt --model Unbabel/wmt22-comet-da
+```
+
+Or using Python:
+
+```python
+from comet import download_model, load_from_checkpoint
+
+model_path = download_model("Unbabel/eamt22-cometinho-da")
+model = load_from_checkpoint(model_path)
+data = [
+    {
+        "src": "Dem Feuer konnte Einhalt geboten werden",
+        "mt": "The fire could be stopped",
+        "ref": "They were able to control the fire."
+    },
+    {
+        "src": "Schulen und Kindergärten wurden eröffnet.",
+        "mt": "Schools and kindergartens were open",
+        "ref": "Schools and kindergartens opened"
+    }
+]
+model_output = model.predict(data, batch_size=8, gpus=1)
+print (model_output)
+```
+
+# Intended uses
+
+Our model is intented to be used for **MT evaluation**. 
+
+Given a a triplet with (source sentence, translation, reference translation) outputs a single score between 0 and 1 where 1 represents a perfect translation. 
+
+# Languages Covered:
+
+This model builds on top of XLM-R which cover the following languages:
+
+Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish.
+
+Thus, results for language pairs containing uncovered languages are unreliable!

checkpoints/model.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4b7f8851e8e4a02082e9b3e3227162dd09c0596387143342eb795812b019f0a0
+size 474246889

hparams.yaml

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+activations: Tanh
+batch_size: 8
+class_identifier: regression_metric
+dropout: 0.1
+encoder_learning_rate: 1.0e-05
+encoder_model: MiniLM
+final_activation: null
+hidden_sizes:
+- 384
+keep_embeddings_frozen: true
+layer: 12
+layerwise_decay: 0.95
+learning_rate: 3.1e-05
+load_weights_from_checkpoint: lightning_logs/cometinho_part-i/checkpoints/epoch=0-step=899999.ckpt
+nr_frozen_epochs: 0.0
+optimizer: AdamW
+pool: avg
+pretrained_model: microsoft/Multilingual-MiniLM-L12-H384
+train_data: data/euro-distil.da.part_ii.csv
+validation_data: data/2019-da-dev.csv