Retrain from scratch on dataset 4 (notebook pipeline)

README.md

@@ -0,0 +1,33 @@
+---
+license: mit
+language: en
+pipeline_tag: token-classification
+tags:
+- ner
+- resume-parsing
+- cv-parser
+base_model: bert-base-uncased
+---
+
+# CV Parser NER — bert-base-uncased (v2)
+
+Token-classification model that extracts **Job Titles**, **Skills**, and
+**Education** from resumes/CVs using a BIO tag scheme.
+
+## Provenance
+- **Trained from scratch on dataset 4** (`resume_bio_annotated_full.csv`,
+  2,483 resumes — 1,739 train / 372 val / 372 test), the team's finalized
+  AI-Studio/Vertex-relabelled dataset.
+- Reproduced end-to-end with the project notebooks/scripts
+  (`retokenize.py` + `train_bert_run.py`).
+- Base model: `bert-base-uncased` · epochs: 4 · learning rate: 2e-5 ·
+  max_length 512 · stride 128 · seed 42.
+
+## Resume-level performance (dataset-4 splits)
+| split | precision | recall | F1 |
+|-------|-----------|--------|----|
+| validation | — | — | 0.5540 |
+| test       | — | — | 0.5852 |
+
+## Labels
+`O, B-JOB_TITLE, I-JOB_TITLE, B-SKILL, I-SKILL, B-EDUCATION, I-EDUCATION`

model.safetensors

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 size 435611468

special_tokens_map.json

@@ -1,7 +1,37 @@
 {
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
 }