add fewshot config with NER.v3

tutorials/llm_clinical_trials/configs/fewshot_drugs_dose.json

@@ -0,0 +1,48 @@
+[
+  {
+    "text": "The patient was given 1mg of paracetamol.",
+    "spans": [
+      {
+        "text": "paracetamol",
+        "is_entity": true,
+        "label": "Drug",
+        "reason": "is a drug name, used as medication"
+      },
+      {
+        "text": "1mg",
+        "is_entity": true,
+        "label": "Dose",
+        "reason": "is the quantity or dose of the given medication"
+      },
+      {
+        "text": "patient",
+        "is_entity": false,
+        "label": "==NONE==",
+        "reason": "is a person, not a drug or dose"
+      }
+    ]
+  },
+  {
+    "text": "Throughout the treatment, they received Aspirin 1mg/kg.",
+    "spans": [
+      {
+        "text": "Aspirin",
+        "is_entity": true,
+        "label": "Drug",
+        "reason": "is a drug brand, used as medication"
+      },
+      {
+        "text": "1mg/kg",
+        "is_entity": true,
+        "label": "Dose",
+        "reason": "is the quantity or dose of the given drug"
+      },
+      {
+        "text": "Aspirin 1mg/kg",
+        "is_entity": false,
+        "label": "==NONE==",
+        "reason": "contains both the drug and the dose - these should be two entities instead"
+      }
+    ]
+  }
+]

tutorials/llm_clinical_trials/configs/ner_fewshot_openai.cfg

@@ -0,0 +1,27 @@
+[nlp]
+lang = "en"
+pipeline = ["llm"]
+batch_size = 128
+
+[components]
+
+[components.llm]
+factory = "llm"
+
+[components.llm.model]
+@llm_models = "spacy.GPT-4.v2"
+config = {"seed": 342, "temperature": 0.0}
+
+[components.llm.task]
+@llm_tasks = "spacy.NER.v3"
+labels = ["Drug", "Dose"]
+description = Entities are drugs or their doses. They can be uppercased, title-cased, or lowercased.
+    Each occurrence of an entity in the text should be extracted.
+
+[components.llm.task.label_definitions]
+Drug = "A medicine or drug given to a patient as a treatment. Can be a generic name or brand name, e.g. paracetamol, Aspirin"
+Dose = "The measured quantity (dose) of a certain medicine given to patients, e.g. 1mg. This should exclude the drug name."
+
+[components.llm.task.examples]
+@misc = "spacy.FewShotReader.v1"
+path = "configs/fewshot_drugs_dose.json"

tutorials/llm_clinical_trials/configs/ner_zeroshot_openai.cfg

@@ -14,4 +14,4 @@ config = {"seed": 342, "temperature": 0.0}
 
 [components.llm.task]
 @llm_tasks = "spacy.NER.v2"
-labels = "Drug,Dose"
+labels = ["Drug", "Dose"]

tutorials/llm_clinical_trials/project.yml

@@ -2,7 +2,7 @@ title: 'Clinical trial results extraction with LLMs'
 description: "Using an LLM in a spaCy pipeline to extract patient groups, treatments and outcomes in clinical trials."
 
 vars:
-  ner_config: "ner_zeroshot_openai.cfg"  # "ner_dolly.cfg"   # "ner_falcon.cfg"
+  ner_config: "ner_fewshot_openai.cfg"   # "ner_zeroshot_openai.cfg"
   trial_config: "trial_openai.cfg"
   pmid: 27144689
 

tutorials/llm_clinical_trials/scripts/visualise_entities.py

@@ -9,25 +9,25 @@
 from wasabi import msg
 
 DEBUG = False
-PRINT_CONSOLE = False
-PRINT_DISPLACY = True
+PRINT_CONSOLE = True
+PRINT_DISPLACY = False
 
 
 def visualise_entities(pmid: int, config_path: Path, verbose: bool = False):
     spacy_llm.logger.addHandler(logging.StreamHandler())
     if DEBUG:
         spacy_llm.logger.setLevel(logging.DEBUG)
 
-    msg.text(f"Processing PMID {pmid}", show=verbose)
-    msg.text(f"Loading config from {config_path}", show=verbose)
+    msg.info(f"Processing PMID {pmid}", show=verbose)
+    msg.info(f"Loading config from {config_path}", show=verbose)
     text = read_trial(pmid, verbose=verbose)
     nlp = assemble(config_path)
     doc = nlp(text)
     ents = list(doc.ents)
     if PRINT_CONSOLE:
-        print("ents", len(ents))
+        msg.text(f" - Number of entities: {len(ents)}")
         for ent in ents:
-            print(ent.text, ent.label_)
+            msg.text(f"    - {ent.text} [{ent.label_}]")
     if PRINT_DISPLACY:
         options = {
             "ents": ["Drug", "Dose"],