Upload MultiClinNER ro/symptom model + card

.gitattributes

@@ -0,0 +1,33 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,118 @@
+---
+language:
+  - ro
+tags:
+  - biomedical
+  - clinical
+  - EHR
+  - NER
+  - named-entity-recognition
+  - token-classification
+  - multiclinner
+  - ro
+  - symptom
+license: cc-by-4.0
+metrics:
+  - f1
+base_model:
+  - FacebookAI/xlm-roberta-base
+pipeline_tag: token-classification
+model-index:
+  - name: BSC-NLP4BIA/multiclinner-ro-symptom-xlm-roberta
+    results:
+      - task:
+          type: token-classification
+        dataset:
+          name: MultiClinNER ro symptom (test)
+          type: MultiClinNER
+        metrics:
+          - name: strict F1
+            type: f1
+            value: 0.5833
+          - name: char F1 (gold)
+            type: f1
+            value: 0.7334
+---
+
+# multiclinner-ro-symptom-xlm-roberta
+
+## Table of contents
+<details>
+<summary>Click to expand</summary>
+
+- [Model description](#model-description)
+- [How to use](#how-to-use)
+- [Evaluation](#evaluation)
+- [Limitations and bias](#limitations-and-bias)
+- [Additional information](#additional-information)
+  - [Authors](#authors)
+  - [Contact information](#contact-information)
+  - [Funding](#funding)
+  - [Citing information](#citing-information)
+  - [Disclaimer](#disclaimer)
+
+</details>
+
+## Model description
+A fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) for **SYMPTOM Named Entity Recognition** in Romanian clinical text. It was developed for MultiClinNER, a subtask of the [MultiClinAI shared task](https://temu.bsc.es/MultiClinAI/) organized by the NLP4BIA team at the Barcelona Supercomputing Center as part of the #SMM4H-HeaRD Workshop at the ACL 2026 conference. The model labels mentions of **SYMPTOM** using the BIO tagging scheme: `O`, `B-SYMPTOM`, `I-SYMPTOM`.
+
+## How to use
+
+⚠ We recommend pre-tokenizing the input text into words instead of providing it directly to the model, as this is how the model was trained. Otherwise, the results and performance might get affected.
+
+```python
+from transformers import pipeline
+
+ner = pipeline(
+    "token-classification",
+    model="BSC-NLP4BIA/multiclinner-ro-symptom-xlm-roberta",
+    aggregation_strategy="simple",
+)
+ner("...clinical text in Romanian...")
+```
+
+## Evaluation
+
+Scores were computed with the official [MultiClinAIEval](https://github.com/nlp4bia-bsc/MultiClinAIEval) library on the MultiClinNER **Romanian** gold-standard test documents for **SYMPTOM** (the shared-task gold set for this language and entity type).
+
+| Metric | Score | Definition |
+|---|---|---|
+| strict F1 | 0.5833 | exact match of both entity span (start/end) and label |
+| char F1 (gold) | 0.7334 | character-level F1, restricted to the gold-standard documents |
+
+See the [MultiClinAI shared task](https://temu.bsc.es/MultiClinAI/) for the full evaluation protocol.
+
+## Limitations and bias
+At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
+
+## Additional information
+
+### Authors
+NLP4BIA team at the Barcelona Supercomputing Center (nlp4bia@bsc.es).
+
+### Contact information
+jan.rodriguez [at] bsc.es
+
+### Funding
+More information will be available soon.
+
+### Citing information
+Please cite the MultiClinAI shared task overview paper (forthcoming).
+
+```bibtex
+
+
+
+
+```
+
+### Disclaimer
+
+The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
+
+When third parties deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence.
+
+---
+Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
+
+Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.

config.json

@@ -0,0 +1,40 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "XLMRobertaForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": 0.5,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-SYMPTOM",
+    "2": "I-SYMPTOM"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "B-SYMPTOM": 1,
+    "I-SYMPTOM": 2,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.4",
+  "type_vocab_size": 1,
+  "use_cache": false,
+  "vocab_size": 250002
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4120326c53deba3267c0df163da9f05f9ba41993d41270588a10621a837f7fad
+size 1109845468

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6c77ee2ef43d82281b6f419aaadb55954ac3faa4ffd2c339b5b33bea9060930
+size 17098186

tokenizer_config.json

@@ -0,0 +1,14 @@
+{
+  "add_prefix_space": true,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "is_local": true,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}