Getting the same value for all evaluation metrics

[ameni-ayedi]

I’m working on finetuning LILT model on a custom dataset where the labels aren’t exactly IOB format .When using seqeval i get an error telling me the tags aren’t NER tags so i switched to loading each metric separately however after training the model using trainer() i kept getting the same value for accuracy f1 precision and recall i don’t understand why especially since the code i have been running worked before :`import evaluate

metric = evaluate.load("seqeval")

precision_metric = evaluate.load("precision") recall_metric = evaluate.load("recall") f1_metric = evaluate.load("f1") accuracy_metric = evaluate.load("accuracy")

import numpy as np

def compute_metrics(eval_pred): predictions, labels = eval_pred predictions = np.argmax(predictions, axis=2)

# Remove ignored index (special tokens)
true_predictions = [
    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
    for prediction, label in zip(predictions, labels)
]
true_labels = [
    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
    for prediction, label in zip(predictions, labels)
]

# flat list
true_predictions = [item for sublist in true_predictions for item in sublist]
true_labels = [item for sublist in true_labels for item in sublist]

results = {

precision_metric.compute(predictions = true_predictions, references = true_labels, average="micro")
recall_metric.compute(predictions = true_predictions, references = true_labels, average="micro")
f1_metric.compute(predictions = true_predictions, references = true_labels, average="micro")

accuracy_metric.compute(predictions = true_predictions, references = true_labels)}

return results

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(output_dir="test", hub_model_id=hub_model_id, num_train_epochs=5, learning_rate=2e-5, evaluation_strategy="steps", eval_steps=5, load_best_model_at_end=True, metric_for_best_model="f1")

from transformers.data.data_collator import default_data_collator

class CustomTrainer(Trainer): def get_train_dataloader(self): return train_dataloader

def get_eval_dataloader(self, eval_dataset = None):
    return eval_dataloader

Initialize our Trainer

trainer = CustomTrainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=tokenizer, compute_metrics=compute_metrics, )`

[ameni-ayedi]

@piegu i would really appreciate your help