Upload folder using huggingface_hub

README.md

@@ -0,0 +1,199 @@
+---
+pipeline_tag: translation
+library_name: comet
+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
+base_model:
+- FacebookAI/xlm-roberta-large
+---
+
+# COMET-poly-ic1-wmt25
+
+This model is based on [COMET-poly](https://github.com/zouharvi/COMET-poly), which is a fork but not compatible with original Unbabel's COMET.
+To run the model, you need to first install this version of COMET either with:
+```bash
+pip install "git+https://github.com/zouharvi/COMET-poly#egg=comet-poly&subdirectory=comet_poly"
+```
+or in editable mode:
+```bash
+git clone https://github.com/zouharvi/COMET-poly.git
+cd COMET-poly
+pip3 install -e comet_poly
+```
+
+This model scores the translation `mt` but takes additional in-context example: source `src2`, translation `mt2`, and score `score2`, which makes it a better quality estimator:
+```python
+import comet_poly
+model = comet_poly.load_from_checkpoint(comet_poly.download_model("zouharvi/COMET-poly-ic1-wmt25"))
+data = [
+    {
+        "src": "Iceberg lettuce got its name in the 1920s when it was shipped packed in ice to stay fresh.",
+        "mt": "Eisbergsalat erhielt seinen Namen in den 1920er-Jahren, als er in Eis verpackt verschickt wurde, um frisch zu bleiben.",
+        "src2": "Lettuce is mostly water, which helps keep it crisp when chilled.",
+        "mt2": "Kopfsalat besteht größtenteils aus Wasser, was ihm hilft, beim Kühlen knackig zu bleiben.",
+        "score2": 94.5
+    },
+    {
+        "src": "Goats have rectangular pupils, which give them a wide field of vision—up to 320 degrees!",
+        "mt": "Kozy mají obdélníkové zornice, což jim umožňuje vidět skoro všude kolem sebe, aniž by musely otáčet hlavou.",
+        "src2": "Sheep, like goats, also have rectangular pupils for better peripheral vision.",
+        "mt2": "Вівці, як і кози, також мають прямокутні зіниці для кращого периферичного зору.",
+        "score2": 96.0
+    },
+    {
+        "src": "This helps them spot predators from almost all directions without moving their heads.",
+        "mt": "Điều này giúp chúng phát hiện kẻ săn mồi từ gần như mọi hướng mà không cần quay đầu.",
+        "src2": "Many prey animals have evolved to detect threats with minimal movement.",
+        "mt2": "Nhiều động vật thịt có tiến hóa để xem mối nguy bằng nhỏ đi lại.",
+        "score2": 42.3
+    }
+]
+print("scores", model.predict(data, batch_size=8, gpus=1).scores)
+```
+Outputs:
+```
+scores [98.09857940673828, 85.52458953857422, 83.38972473144531]
+```
+
+You can use a readily-available training data to do the on-the-fly retrieval.
+Specifically, this model has been trained with retrieval based on `src`:
+```python
+import datasets
+import comet_poly.retrieval
+data = [
+    {
+        "src": "Iceberg lettuce got its name in the 1920s when it was shipped packed in ice to stay fresh.",
+        "mt": "Eisbergsalat erhielt seinen Namen in den 1920er-Jahren, als er in Eis verpackt verschickt wurde, um frisch zu bleiben.",
+    },
+    {
+        "src": "Goats have rectangular pupils, which give them a wide field of vision—up to 320 degrees!",
+        "mt": "Kozy mají obdélníkové zornice, což jim umožňuje vidět skoro všude kolem sebe, aniž by musely otáčet hlavou.",
+    },
+    {
+        "src": "This helps them spot predators from almost all directions without moving their heads.",
+        "mt": "Điều này giúp chúng phát hiện kẻ săn mồi từ gần như mọi hướng mà không cần quay đầu.",
+    }
+]
+
+data_kb = list(datasets.load_dataset("zouharvi/wmt-human-all", split="train"))
+data_retrieved = comet_poly.retrieval.retrieve_from_kb(
+    data=data,
+    data_kb=data_kb,
+    k=1,
+    prevent_hardmatch=False,
+    key="src",
+)
+# add the retrieved data
+for line, lines_retrieved in zip(data, data_retrieved):
+    for i in range(len(lines_retrieved)):
+        line[f"src{i+2}"] = lines_retrieved[i]["src"]
+        line[f"mt{i+2}"] = lines_retrieved[i]["mt"]
+        line[f"score{i+2}"] = lines_retrieved[i]["score"]
+
+print("scores", model.predict(data, batch_size=8, gpus=1).scores)
+```
+
+The training data is WMT up to 2024 (inclusive) with DA/ESA/MQM merged on a single scale.
+This model is based on the work [TODO](TODO) which can be cited as:
+```
+@misc{zuefle2025comet,
+    title={COMET-poly: Machine Translation Metric Grounded in Other Candidates},
+    author={Maike Züfle, Vilém Zouhar, Tu Anh Dinh, Felipe Polo, Jan Niehues, Mrinmaya Sachan},
+    year={2025},
+}
+```
+

checkpoints/model.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d691b8ffe2258f31ed43a8eae61d9af0b2ad4a6900d0e355428d435edd70bec1
+size 2478854385

hparams.yaml

@@ -0,0 +1,36 @@
+activations: Tanh
+additional_translation_in:
+- true
+- true
+- true
+- false
+- false
+backprop_additional_translation: true
+batch_size: 8
+class_identifier: polyic_metric
+dropout: 0.1
+encoder_learning_rate: 1.0e-06
+encoder_model: XLM-RoBERTa
+final_activation: null
+hidden_sizes:
+- 2048
+- 1024
+keep_embeddings_frozen: true
+layer: mix
+layer_norm: false
+layer_transformation: sparsemax
+layerwise_decay: 0.95
+learning_rate: 1.5e-05
+load_pretrained_weights: true
+local_files_only: false
+loss: mse
+nr_frozen_epochs: 0.3
+optimizer: AdamW
+pool: avg
+pretrained_model: xlm-roberta-large
+train_data:
+- data/csv/train_retrieval_minilm_11_src_wmt25.csv
+use_ref: false
+validation_data:
+- data/csv/dev_retrieval_minilm_11_src.csv
+warmup_steps: 0