remove stuff

cometda.py

@@ -1,81 +0,0 @@
-import os
-import pathlib
-
-import datasets
-import evaluate
-from huggingface_hub import snapshot_download, login
-
-from comet.models.multitask.unified_metric import UnifiedMetric
-
-
-login(token="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")
-
-_CITATION = """\
-@inproceedings{rei-etal-2022-comet,
-    title = "{COMET}-22: Unbabel-{IST} 2022 Submission for the Metrics Shared Task",
-    author = "Rei, Ricardo  and
-      C. de Souza, Jos{\'e} G.  and
-      Alves, Duarte  and
-      Zerva, Chrysoula  and
-      Farinha, Ana C  and
-      Glushkova, Taisiya  and
-      Lavie, Alon  and
-      Coheur, Luisa  and
-      Martins, Andr{\'e} F. T.",
-    booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
-    month = dec,
-    year = "2022",
-    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.wmt-1.52",
-    pages = "578--585",
-}
-"""
-
-
-_DESCRIPTION = """\
-From https://huggingface.co/Unbabel/unite-mup
-"""
-
-class COMETDA(evaluate.Metric):
-    def _info(self):
-        return evaluate.MetricInfo(
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            features=datasets.Features(
-                {
-                    "predictions": datasets.Value("string"),
-                    "references": datasets.Value("string"),
-                }
-            ),
-        )
-        
-    def _download_and_prepare(self, dl_manager):
-        try:
-            model_checkpoint_path = next(pathlib.Path('./models--Unbabel--wmt22-cometkiwi-da/').rglob('*.ckpt'))
-            self.model = UnifiedMetric.load_from_checkpoint(model_checkpoint_path)
-        except:
-            model_path = snapshot_download(repo_id="Unbabel/wmt22-cometkiwi-da", cache_dir=os.path.abspath(os.path.dirname('.')))
-            model_checkpoint_path = f"{model_path}/checkpoints/model.ckpt"
-            self.model = UnifiedMetric.load_from_checkpoint(model_checkpoint_path)
-
-
-    def _compute(
-        self,
-        predictions,
-        references,
-        data_keys=None,
-    ):  # Allows user to use either source inputs or reference translations as ground truth.
-        data = [{data_keys[0]: p, data_keys[1]: r} for p, r in zip(predictions, references)]
-        return {"scores": self.model.predict(data, batch_size=8).scores}
-
-
-    def compute_triplet(
-        self,
-        predictions,
-        references,
-        sources,
-    ):  # Unified scores, uses sources, hypotheses and references.
-        data = [{"src": s, "mt": p, "ref": r} for s, p, r in zip(sources, predictions, references)]
-        return {"scores": self.model.predict(data, batch_size=8).metadata.unified_scores}
-