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config.json

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+{
+  "architectures": [
+    "XLMEduModelHF"
+  ],
+  "encoder_name": "FacebookAI/xlm-roberta-large",
+  "id2label": {
+    "0": "B-EDU",
+    "1": "I-EDU",
+    "2": "O"
+  },
+  "label2id": {
+    "B-EDU": 0,
+    "I-EDU": 1,
+    "O": 2
+  },
+  "model_type": "xlm_edu",
+  "num_tags": 3,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86fb9cf8cad3bf2455dba40d0e20ac686459c52a67d9748f1e3bde9b254809d8
+size 2239623056

modeling_xlmedu.py

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+"""
+HuggingFace-compatible wrapper for XLMEduModel.
+
+Architecture: XLM-RoBERTa-large encoder → linear classifier → CRF (torchcrf)
+Task: BIO token classification for situation-entity segmentation (B-EDU / I-EDU / O)
+
+Loading from the Hub:
+    from modeling_xlmedu import XLMEduConfig, XLMEduModelHF
+    config = XLMEduConfig.from_pretrained("your-username/your-repo")
+    model  = XLMEduModelHF.from_pretrained("your-username/your-repo", config=config)
+
+Inference:
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-large")
+    inputs    = tokenizer("Hello world.", return_tensors="pt")
+    tag_ids   = model.predict(inputs["input_ids"], inputs["attention_mask"])
+    tags      = [model.config.id2label[i] for i in tag_ids[0]]
+"""
+
+from __future__ import annotations
+
+from typing import Dict, List, Optional
+
+import torch
+from torchcrf import CRF
+from transformers import AutoModel, PretrainedConfig, PreTrainedModel
+
+
+LABELS   = {"B-EDU": 0, "I-EDU": 1, "O": 2}
+BIO_TAGS = [tag for tag, _ in sorted(LABELS.items(), key=lambda x: x[1])]
+NUM_TAGS = len(BIO_TAGS)
+O_IDX    = LABELS["O"]
+
+
+class XLMEduConfig(PretrainedConfig):
+    model_type = "xlm_edu"
+
+    def __init__(
+        self,
+        encoder_name: str = "FacebookAI/xlm-roberta-large",
+        num_tags: int = NUM_TAGS,
+        label2id: Optional[Dict[str, int]] = None,
+        id2label: Optional[Dict[int, str]] = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.encoder_name = encoder_name
+        self.num_tags     = num_tags
+        self.label2id     = label2id or LABELS
+        self.id2label     = id2label or {v: k for k, v in LABELS.items()}
+
+
+class XLMEduModelHF(PreTrainedModel):
+    config_class = XLMEduConfig
+
+    def __init__(self, config: XLMEduConfig):
+        super().__init__(config)
+        self.encoder    = AutoModel.from_pretrained(config.encoder_name, output_hidden_states=False)
+        self.classifier = torch.nn.Linear(self.encoder.config.hidden_size, config.num_tags)
+        self.crf        = CRF(config.num_tags, batch_first=True)
+
+    def forward(
+        self,
+        input_ids:      torch.Tensor,
+        attention_mask: torch.Tensor,
+        labels:         Optional[torch.Tensor] = None,
+    ):
+        """
+        Returns a dict with 'loss' (if labels given) and 'logits' (emission scores).
+        logits shape: (batch, seq_len, num_tags)
+        """
+        outputs    = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        emissions  = self.classifier(outputs.last_hidden_state)
+
+        loss = None
+        if labels is not None:
+            crf_mask   = attention_mask.bool()
+            labels_crf = labels.clone()
+            labels_crf[labels_crf == -100] = O_IDX
+            loss = -self.crf(emissions.float(), labels_crf, mask=crf_mask, reduction="mean")
+
+        return {"loss": loss, "logits": emissions}
+
+    def predict(
+        self,
+        input_ids:      torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> List[List[int]]:
+        """Viterbi-decode the best tag sequence for each sample in the batch."""
+        emissions = self.forward(input_ids, attention_mask)["logits"]
+        return self.crf.decode(emissions.float(), mask=attention_mask.bool())

run_config.json

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+{
+  "data_dir": "/hnvme/workspace/v110ee23-edu_segmentation/sitentsegm/data/processed_sentences/xlm-roberta-large",
+  "output_dir": "/hnvme/workspace/v110ee23-edu_segmentation/sitentsegm/results/experiment_314223/xlm-roberta-large_lr4e-5_ep20_wd0.005_se84",
+  "model": "FacebookAI/xlm-roberta-large",
+  "epochs": 20,
+  "batch_size": 64,
+  "lr": 4e-05,
+  "weight_decay": 0.005,
+  "seed": 84,
+  "patience": 3,
+  "fp16": true,
+  "grad_accum": 1,
+  "local_rank": -1,
+  "model_slug": "xlm-roberta-large",
+  "model_resolved": "/hnvme/workspace/v110ee23-edu_segmentation/.cache/hub/models--FacebookAI--xlm-roberta-large/snapshots/c23d21b0620b635a76227c604d44e43a9f0ee389"
+}