File size: 4,187 Bytes
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Test script for ConceptFrameMet model
This script tests basic model loading and inference capabilities.
"""
import torch
from transformers import RobertaTokenizer
import json
import sys
import os
print("="*60)
print("ConceptFrameMet Model Test")
print("="*60)
# Set model path
model_path = "/data/gpfs/projects/punim0478/otmakhovay/ConceptFrameMet"
print(f"\n1. Testing file presence...")
required_files = [
"pytorch_model.bin",
"config.json",
"vocab.json",
"merges.txt"
]
for file in required_files:
filepath = os.path.join(model_path, file)
if os.path.exists(filepath):
size = os.path.getsize(filepath)
size_mb = size / (1024 * 1024)
print(f" ✓ {file}: {size_mb:.2f} MB")
else:
print(f" ✗ {file}: MISSING")
sys.exit(1)
print(f"\n2. Loading tokenizer...")
try:
tokenizer = RobertaTokenizer.from_pretrained(model_path)
print(f" ✓ Tokenizer loaded successfully")
print(f" - Vocab size: {tokenizer.vocab_size}")
except Exception as e:
print(f" ✗ Error loading tokenizer: {e}")
sys.exit(1)
print(f"\n3. Loading config...")
try:
with open(f"{model_path}/config.json", 'r') as f:
config = json.load(f)
print(f" ✓ Config loaded successfully")
print(f" - Model type: {config.get('model_type', 'roberta')}")
print(f" - Hidden size: {config.get('hidden_size', 768)}")
print(f" - Layers: {config.get('num_hidden_layers', 12)}")
except Exception as e:
print(f" ✗ Error loading config: {e}")
sys.exit(1)
print(f"\n4. Loading model weights...")
try:
state_dict = torch.load(f"{model_path}/pytorch_model.bin", map_location='cpu')
print(f" ✓ Model weights loaded successfully")
print(f" - Number of parameters: {len(state_dict)}")
# Show some key layers
print(f" - Sample layers:")
for i, key in enumerate(list(state_dict.keys())[:5]):
shape = state_dict[key].shape if hasattr(state_dict[key], 'shape') else 'scalar'
print(f" • {key}: {shape}")
except Exception as e:
print(f" ✗ Error loading weights: {e}")
sys.exit(1)
print(f"\n5. Testing tokenization...")
try:
test_sentence = "The company is navigating through troubled waters"
test_target = "navigating"
# Tokenize sentence
inputs = tokenizer(
test_sentence,
max_length=150,
padding='max_length',
truncation=True,
return_tensors='pt'
)
print(f" ✓ Tokenization successful")
print(f" - Sentence: '{test_sentence}'")
print(f" - Target: '{test_target}'")
print(f" - Input shape: {inputs['input_ids'].shape}")
# Find target positions
target_tokens = tokenizer.tokenize(test_target)
sentence_tokens = tokenizer.tokenize(test_sentence)
target_positions = []
for i in range(len(sentence_tokens) - len(target_tokens) + 1):
if sentence_tokens[i:i+len(target_tokens)] == target_tokens:
target_positions = list(range(i+1, i+1+len(target_tokens)))
break
print(f" - Target found at positions: {target_positions}")
except Exception as e:
print(f" ✗ Error during tokenization: {e}")
sys.exit(1)
print(f"\n6. Checking model compatibility...")
try:
from modeling_conceptframemet import ConceptFrameMetForMetaphorDetection
print(f" ✓ Custom model class can be imported")
except Exception as e:
print(f" ⚠ Warning: Could not import custom model class: {e}")
print(f" This is OK - the model can still be used with standard transformers")
print(f"\n" + "="*60)
print("✓ ALL TESTS PASSED!")
print("="*60)
print(f"\nYour ConceptFrameMet model is ready for upload to Hugging Face!")
print(f"\nModel summary:")
print(f" - Location: {model_path}")
print(f" - Total size: ~1.5 GB")
print(f" - Base model: RoBERTa-base")
print(f" - Epoch: 3 (best checkpoint)")
print(f" - Capabilities:")
print(f" • Metaphor detection")
print(f" • Frame prediction (with nixie1981/sem_frames)")
print(f" • Source domain prediction")
print(f"\nNext step: Follow HUGGINGFACE_UPLOAD_GUIDE.md to upload!")
print("="*60)
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