Named Entity Resolution with dslim/distilbert-NER

llms/bert-ner.md

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+# Named Entity Resolution with dslim/distilbert-NER
+
+I was exploring the original BERT model from 2018, which is mainly useful if you fine-tune a model on top of it for a specific task.
+
+[dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) by David S. Lim is a popular implementation of this, with around 20,000 downloads from Hugging Face every month.
+
+I tried the demo from the README but it didn't quite work - it complained about an incompatibility with Numpy 2.0.
+
+So I used `uv run --with 'numpy<2.0'` to run it in a temporary virtual environment. Here's a Bash one-liner that demonstrated the model:
+
+```bash
+uv run --with 'numpy<2.0' --with transformers python -c '
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+import json
+model = AutoModelForTokenClassification.from_pretrained("dslim/distilbert-NER")
+tokenizer = AutoTokenizer.from_pretrained("dslim/distilbert-NER")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+text = "This is an example sentence about Simon Willison who lives in Half Moon Bay"
+print(json.dumps(nlp(text), indent=2, default=repr))'
+```
+The first time you run this it will download 250MB to your `~/.cache/huggingface/hub/models--dslim--distilbert-NER` folder.
+
+Example output:
+
+```json
+[
+  {
+    "entity": "B-PER",
+    "score": "0.9982101",
+    "index": 7,
+    "word": "Simon",
+    "start": 34,
+    "end": 39
+  },
+  {
+    "entity": "I-PER",
+    "score": "0.99835676",
+    "index": 8,
+    "word": "Willis",
+    "start": 40,
+    "end": 46
+  },
+  {
+    "entity": "I-PER",
+    "score": "0.9977602",
+    "index": 9,
+    "word": "##on",
+    "start": 46,
+    "end": 48
+  },
+  {
+    "entity": "B-LOC",
+    "score": "0.99432063",
+    "index": 13,
+    "word": "Half",
+    "start": 62,
+    "end": 66
+  },
+  {
+    "entity": "I-LOC",
+    "score": "0.99325883",
+    "index": 14,
+    "word": "Moon",
+    "start": 67,
+    "end": 71
+  },
+  {
+    "entity": "I-LOC",
+    "score": "0.9919292",
+    "index": 15,
+    "word": "Bay",
+    "start": 72,
+    "end": 75
+  }
+]
+```