diff --git a/.gitignore b/.gitignore
index 9d2190d30ae245535711870097ddd51e409c8d98..f2ca767d25c287427f9c7d41dcf7b9d80e68aff8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -116,3 +116,6 @@ train.json
# slurm output
logs
+
+# evaluation output
+results
diff --git a/shell.nix b/shell.nix
index 1fb86205b49a2fdb2b63848d3d64c6e29a274aa9..90bd06e0eacb4dd703a6e91c0f433b0bac7127be 100644
--- a/shell.nix
+++ b/shell.nix
@@ -2,29 +2,15 @@
let
myPythonPackages = ps: with ps; [
pip
- python-dotenv
- numpy
- ollama
- datasets
- protobuf
- requests
- tqdm
- #transformers
- #gensim
- #accelerate
- #gliner missing
];
- fhs = pkgs.buildFHSEnv {
- name = "Python NER environment with pip";
-
- targetPkgs = _: [
- (pkgs.python3.withPackages myPythonPackages)
- ];
-
- profile =''
- python -m venv .venv
- source .venv/bin/activate
- zsh
- '';
- };
-in fhs.env
+in pkgs.mkShell {
+ buildInputs = with pkgs; [
+ (python3.withPackages myPythonPackages)
+ ];
+ shellHook = ''
+ export LD_LIBRARY_PATH=$NIX_LD_LIBRARY_PATH
+ python -m venv .venv
+ source .venv/bin/activate
+ zsh
+ '';
+}
diff --git a/src/common_interface.py b/src/common_interface.py
index dc6c73cd9c4d765d5bf75a801b9a109974b88dcc..e5c6a701b88c8f8207d75289624493283d7b1043 100644
--- a/src/common_interface.py
+++ b/src/common_interface.py
@@ -7,6 +7,8 @@ from src.models.GLiNER import find_entities as find_entities_gliner
from src.models.GLiNER import classify_entity as classify_entity_gliner
from src.models.T5_NLI import classify_entity as classify_entity_t5_nli
from src.models.T5_MLM_label import classify_entity as classify_entity_t5_mlm_label
+from src.models.T5_MLM_entity import classify_entity as classify_entity_t5_mlm_entity
+from src.models.T5_MLM_entity import set_label_dict as set_label_dict_t5_mlm_entity
from src.models.Word2Vec import classify_entity as classify_entity_word2vec
from src.models.Word2Vec import set_label_dict as set_label_dict_word2vec
from src.experiments.NER_with_LLMs.NER_with_LLMs import find_entities as find_entities_llm
@@ -21,6 +23,8 @@ def classify_entity(model_name, sentence, entity, labels):
return classify_entity_t5_nli(sentence, entity, labels)
elif model_name == "T5-MLM-label":
return classify_entity_t5_mlm_label(sentence, entity, labels)
+ elif model_name == "T5-MLM-entity":
+ return classify_entity_t5_mlm_entity(sentence, entity, labels)
elif model_name == "GLiNER":
return classify_entity_gliner(sentence, entity, labels)
elif model_name == "Word2Vec":
@@ -49,5 +53,7 @@ def set_label_dict(model_name, label_dict):
"""
if model_name == "Word2Vec":
return set_label_dict_word2vec(label_dict)
+ elif model_name == "T5-MLM-entity":
+ return set_label_dict_t5_mlm_entity(label_dict)
else:
print(f"set_label_dict not implemented for {model_name}")
diff --git a/src/experiments/NEC_evaluation/evaluation.py b/src/experiments/NEC_evaluation/evaluation.py
index 748e56f3c75cf2a85b72b9a4818115d6a972bbf8..54477801b47b5c696eea9a0dd924168d7c2dd303 100644
--- a/src/experiments/NEC_evaluation/evaluation.py
+++ b/src/experiments/NEC_evaluation/evaluation.py
@@ -27,7 +27,7 @@ def run_NEC_tests(model_name, dataset, results_dir, test_instances=10):
labels = data_manager.get_labels(dataset)
data = data_manager.get_annotated_sentences(dataset, test_instances)
- if (model_name == "Word2Vec"):
+ if (model_name == "Word2Vec" or model_name == "T5-MLM-entity"):
label_dict = data_manager.get_label_dict(dataset, test_instances)
print(label_dict)
set_label_dict(model_name, label_dict)
@@ -58,7 +58,7 @@ def run_NEC_tests(model_name, dataset, results_dir, test_instances=10):
def run_NEC_tests_all():
- models = ["GLiNER", "Llama-3.1-8B", "T5-NLI", "T5-MLM-label", "Word2Vec", "DeepSeek-R1-Distill-Qwen-32B"]
+ models = ["GLiNER", "Llama-3.1-8B", "T5-NLI", "T5-MLM-label", "T5-MLM-entity", "Word2Vec", "DeepSeek-R1-Distill-Qwen-32B"]
datasets = ["CoNLL", "FIGER-coarse", "FIGER-fine"] # "Pile-NER-type"]
for model in models:
for dataset in datasets:
diff --git a/src/experiments/finetune_T5/finetune_T5_MLM_entity.py b/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7ac094afa25b2269c700b71a2949c05a8effbd5
--- /dev/null
+++ b/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
@@ -0,0 +1,33 @@
+import data.data_manager as data_manager
+from src.models.T5_MLM_entity import finetune_model, set_label_dict
+
+def finetune_t5(dataset):
+ print("start")
+ annotated_sentences = data_manager.get_annotated_sentences(dataset, 1000)
+ labels = data_manager.get_labels(dataset)
+
+ sentences = []
+ entities = []
+ labels = []
+
+ for annotated_sentence in annotated_sentences:
+ sentence = annotated_sentence[0]
+
+ for annotation in annotated_sentence[1]:
+ sentences.append(sentence)
+ entities.append(annotation[0])
+ labels.append(annotation[1])
+
+ print(f"Finetuning on {len(sentences)} examples")
+
+ for i in range(min(len(sentences), 50)):
+ print(f"sentence: {sentences[i]}, entity: {entities[i]}, label: {labels[i]}")
+
+ epochs = 20
+
+
+ label_dict = data_manager.get_label_dict(dataset, 1000)
+ set_label_dict(label_dict)
+ finetune_model(sentences, entities, labels, output_dir=f"./src/models/t5_mlm_entity_finetuned_model/pretrained_{dataset}_epoch{epochs}", epochs=epochs)
+
+finetune_t5("CoNLL")
diff --git a/src/experiments/finetune_T5/finetune_T5_MLM.py b/src/experiments/finetune_T5/finetune_T5_MLM_label.py
similarity index 100%
rename from src/experiments/finetune_T5/finetune_T5_MLM.py
rename to src/experiments/finetune_T5/finetune_T5_MLM_label.py
diff --git a/src/models/T5_MLM_entity.py b/src/models/T5_MLM_entity.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9604cbc343be57f4533f7d6620c6beed49dc933
--- /dev/null
+++ b/src/models/T5_MLM_entity.py
@@ -0,0 +1,115 @@
+import torch
+import random
+import numpy as np
+from torch.nn.functional import softmax
+from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments, DataCollatorForSeq2Seq
+from datasets import Dataset, DatasetDict
+
+model_name = "google-t5/t5-base"
+
+print("Loading model: T5 MLM entity")
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+print("Finished loading model: T5 MLM entity")
+
+def set_label_dict(label_dict):
+ global label_representatives
+ label_representatives = label_dict
+
+def classify_entity(sentence, entity, labels):
+ global label_representatives
+
+ masked_sentence = sentence.replace(entity, "<extra_id_0>", 1)
+ input_ids = tokenizer(masked_sentence, return_tensors="pt").input_ids
+
+ best_label = None
+ best_label_loss = float("inf")
+
+ for label in labels:
+ best_rep_loss = float("inf")
+ representatives = label_representatives[label]
+
+ for representative in representatives:
+ # do not cheat if entity is one of the representatives already
+ if entity == representative:
+ continue
+
+ representative_ids = tokenizer(f"<extra_id_0> {representative} <extra_id_1>", return_tensors="pt").input_ids
+ loss = model(input_ids=input_ids, labels=representative_ids).loss.item()
+ best_rep_loss = min(best_rep_loss, loss)
+
+ if best_rep_loss < best_label_loss:
+ best_label_loss = best_rep_loss
+ best_label = label
+
+ return best_label if best_label else labels[0]
+
+
+def finetune_model(sentences, entities, labels, output_dir, epochs=10):
+ input_texts = []
+ target_texts = []
+
+ random.seed(42)
+
+ for i in range(len(sentences)):
+ sentence = sentences[i]
+ entity = entities[i]
+ label = labels[i]
+ representatives = label_representatives[label]
+ representative = random.choice(list(representatives))
+ input_texts.append(sentence.replace(entity, "<extra_id_0>", 1))
+ target_texts.append(f"<extra_id_0> {representative} <extra_id_1>")
+
+ model_input = tokenizer(input_texts, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
+ targets = tokenizer(target_texts, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
+
+ model_input["input_ids"] = np.array(model_input["input_ids"])
+ model_input["attention_mask"] = np.array(model_input["attention_mask"])
+ model_input["labels"] = np.array(targets["input_ids"])
+
+ dataset = Dataset.from_dict({
+ "input_ids": model_input["input_ids"],
+ "attention_mask": model_input["attention_mask"],
+ "labels": model_input["labels"]
+ })
+
+ print(dataset)
+
+ # split into training and validation data (20% used for validation)
+ train_test_split = dataset.train_test_split(test_size=0.2, shuffle=True, seed=0)
+
+ dataset = DatasetDict({
+ "train": train_test_split["train"],
+ "validation": train_test_split["test"]
+ })
+
+ dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])
+
+ training_args = TrainingArguments(
+ output_dir="./src/models/t5_nli_finetuned_model",
+ eval_strategy="epoch",
+ learning_rate=5e-5,
+ per_device_train_batch_size=8,
+ per_device_eval_batch_size=8,
+ num_train_epochs=epochs,
+ weight_decay=0.01,
+ save_strategy="no",
+ push_to_hub=False,
+ logging_dir="./logs",
+ )
+
+ data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+
+ trainer = Trainer(
+ model=model,
+ args=training_args,
+ train_dataset=dataset["train"],
+ eval_dataset=dataset["validation"],
+ tokenizer=tokenizer,
+ data_collator=data_collator,
+ )
+
+ trainer.train()
+
+ model.save_pretrained(output_dir)
+ tokenizer.save_pretrained(output_dir)
diff --git a/src/models/T5_MLM_label.py b/src/models/T5_MLM_label.py
index 279416f6384862d46726ed5f48f8d6108368090e..d5fa033433015e6d2fb00a4ba42be9a3102f6a6b 100644
--- a/src/models/T5_MLM_label.py
+++ b/src/models/T5_MLM_label.py
@@ -6,10 +6,10 @@ from datasets import Dataset, DatasetDict
model_name = "google-t5/t5-large"
-print("Loading model: T5 MLM")
+print("Loading model: T5 MLM label")
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
-print("Finished loading model: T5 MLM")
+print("Finished loading model: T5 MLM label")
def classify_entity(sentence, entity, labels):
sentence_with_masked_hypothesis = f"{sentence} {entity} is a <extra_id_0>."