Create README.md

README.md

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+
+# Dataset Card for Text2Tech Curated Documents
+
+## Dataset Summary
+
+This dataset is the result of converting a UIMA CAS 0.4 JSON export from the Inception annotation tool into a simplified format suitable for Natural Language Processing tasks. Specifically, it provides configurations for Named Entity Recognition (NER), Entity Linking (EL), and Relation Extraction (RE).
+
+The conversion process utilized the `dkpro-cassis` library to load the original annotations and `spaCy` for tokenization. The final dataset is structured similarly to the DFKI-SLT/mobie dataset to ensure compatibility and ease of use with the Hugging Face ecosystem.
+
+This version of the dataset loader provides configurations for:
+
+* **Named Entity Recognition (ner)**: NER tags use spaCy's BILUO tagging scheme.
+* **Entity Linking (el)**: Entity mentions are linked to external knowledge bases.
+* **Relation Extraction (re)**: Relations between entities are annotated.
+
+## Supported Tasks and Leaderboards
+
+* **Tasks**: Named Entity Recognition, Entity Linking, Relation Extraction
+* **Leaderboards**: More Information Needed
+
+## Languages
+
+The text in the dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+#### ner
+
+An example of 'train' looks as follows.
+
+```json
+{
+  "docid": "138",
+  "tokens": [
+    "\"",
+    "Samsung",
+    "takes",
+    "aim",
+    "at",
+    "blood",
+    "pressure",
+    "monitoring",
+    "with",
+    "the",
+    "Galaxy",
+    "Watch",
+    "Active",
+    "..."
+  ],
+  "ner_tags": [
+    0,
+    1,
+    0,
+    0,
+    0,
+    2,
+    3,
+    4,
+    0,
+    0,
+    5,
+    6,
+    7,
+    "..."
+  ]
+}
+```
+
+#### el
+
+An example of 'train' looks as follows.
+
+```json
+{
+  "docid": "138",
+  "tokens": [
+      "\"",
+      "Samsung",
+      "takes",
+      "aim",
+      "at",
+      "blood",
+      "pressure",
+      "monitoring",
+      "with",
+      "the",
+      "Galaxy",
+      "Watch",
+      "Active",
+       "..."
+  ],
+    "ner_tags": [
+      0,
+      1,
+      0,
+      0,
+      0,
+      2,
+      3,
+      4,
+      0,
+      0,
+      5,
+      6,
+      7,
+      "..."
+],
+
+  "entity_mentions": [
+    {
+      "text": "Samsung",
+      "start": 1,
+      "end": 2,
+      "char_start": 1,
+      "char_end": 8,
+      "type": 0,
+      "entity_id": "http://www.wikidata.org/entity/Q124989916"
+    },
+    "..."
+  ]
+}
+```
+
+#### re
+
+An example of 'train' looks as follows.
+
+```json
+{
+  "docid": "138",
+  "tokens": [
+    "\"",
+    "Samsung",
+    "takes",
+    "aim",
+    "at",
+    "blood",
+    "pressure",
+    "monitoring",
+    "with",
+    "the",
+    "Galaxy",
+    "Watch",
+    "Active",
+    "..."
+  ],
+  "ner_tags": [
+    0,
+    1,
+    0,
+    0,
+    0,
+    2,
+    3,
+    4,
+    0,
+    0,
+    5,
+    6,
+    7,
+    "..."
+  ],
+  "relations": [
+    {
+      "id": "138-0",
+      "head_start": 706,
+      "head_end": 708,
+      "head_type": 2,
+      "tail_start": 706,
+      "tail_end": 708,
+      "tail_type": 2,
+      "type": 0
+    },
+    "..."
+  ]
+}
+```
+
+### Data Fields
+
+#### ner
+
+* `docid`: A `string` feature representing the document identifier.
+* `tokens`: A `list` of `string` features representing the tokens in the document.
+* `ner_tags`: A `list` of classification labels using spaCy's BILUO tagging scheme. The mapping from ID to tag is as follows:
+
+**BILUO Tagging Scheme:**
+- **B-** (Begin): First token of a multi-token entity
+- **I-** (Inside): Inner tokens of a multi-token entity  
+- **L-** (Last): Final token of a multi-token entity
+- **U-** (Unit): Single token entity
+- **O** (Outside): Non-entity token
+
+```json
+{
+  "O": 0,
+  "U-Organization": 1,
+  "B-Method": 2,
+  "I-Method": 3,
+  "L-Method": 4,
+  "B-Technological System": 5,
+  "I-Technological System": 6,
+  "L-Technological System": 7,
+  "U-Technological System": 8,
+  "U-Method": 9,
+  "B-Material": 10,
+  "L-Material": 11,
+  "I-Material": 12,
+  "B-Organization": 13,
+  "L-Organization": 14,
+  "I-Organization": 15,
+  "U-Material": 16,
+  "B-Technical Field": 17,
+  "L-Technical Field": 18,
+  "I-Technical Field": 19,
+  "U-Technical Field": 20
+}
+```
+
+#### el
+
+* `docid`: A `string` feature representing the document identifier.
+* `tokens`: A `list` of `string` features representing the tokens in the document.
+* `entity_mentions`: A `list` of `struct` features containing:
+   * `text`: a `string` feature.
+   * `start`: token offset start, a `int32` feature.
+   * `end`: token offset end, a `int32` feature.
+   * `char_start`: character offset start, a `int32` feature.
+   * `char_end`: character offset end, a `int32` feature.
+   * `type`: a classification label. The mapping from ID to entity type is as follows:
+
+```json
+{
+  "Organization": 0,
+  "Method": 1,
+  "Technological System": 2,
+  "Material": 3,
+  "Technical Field": 4
+}
+```
+
+   * `entity_id`: a `string` feature representing the entity identifier from a knowledge base.
+
+#### re
+
+* `docid`: A `string` feature representing the document identifier.
+* `tokens`: A `list` of `string` features representing the tokens in the document.
+* `ner_tags`: A `list` of classification labels, corresponding to the NER task.
+* `relations`: A `list` of `struct` features containing:
+   * `id`: a `string` feature representing the relation identifier.
+   * `head_start`: token offset start of the head entity, an `int32` feature.
+   * `head_end`: token offset end of the head entity, an `int32` feature.
+   * `head_type`: a classification label for the head entity type.
+   * `tail_start`: token offset start of the tail entity, an `int32` feature.
+   * `tail_end`: token offset end of the tail entity, an `int32` feature.
+   * `tail_type`: a classification label for the tail entity type.
+   * `type`: a classification label for the relation type. The mapping from ID to relation type is as follows:
+
+```json
+{
+  "ts:executes": 0,
+  "org:develops_or_provides": 1,
+  "ts:contains": 2,
+  "ts:made_of": 3,
+  "ts:uses": 4,
+  "ts:supports": 5,
+  "met:employs": 6,
+  "met:processes": 7,
+  "mat:transformed_to": 8,
+  "org:collaborates": 9,
+  "met:creates": 10,
+  "met:applied_to": 11,
+  "ts:processes": 12
+}
+```
+
+### Data Splits
+
+Please add information about your data splits here. For example:
+
+  * **train**: X samples
+  * **validation**: Y samples
+  * **test**: Z samples
+
+## Dataset Creation
+
+The dataset was created by converting JSON files exported from the Inception annotation tool. The `inception_converter.py` script was used to process these files. This script uses the `dkpro-cassis` library to load the UIMA CAS JSON data and `spaCy` for tokenization and creating BIO tags for the NER task. The data was then split into three separate files for NER, EL, and RE tasks.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+More Information Needed
+
+### Discussion of Biases
+
+More Information Needed
+
+### Other Known Limitations
+
+More Information Needed
+
+## Additional Information
+
+### Dataset Curators
+
+Amir Safari
+
+### Licensing Information
+
+Please specify the license for this dataset.
+
+### Citation Information
+
+Please provide a BibTeX citation for your dataset.
+
+```bibtex
+  author    = {Amir Safari},
+  title     = {Text2Tech Curated Documents},
+  year      = {2025},
+  publisher = {Hugging Face}
+}
+```