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t5-small-60M-esg-keyword

Text2Text Generation
Transformers
Safetensors
English
t5
esg
keyword-extraction
text-generation-inference
Inference Endpoints
Model card Files Community
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0ab2514
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  1. trainig-model/meu_modelo_treinado/config.json +61 -0
  2. trainig-model/meu_modelo_treinado/generation_config.json +7 -0
  3. trainig-model/meu_modelo_treinado/model.safetensors +3 -0
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  7. trainig-model/results/checkpoint-2721/config.json +61 -0
  8. trainig-model/results/checkpoint-2721/generation_config.json +7 -0
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  17. trainig-model/results/checkpoint-2721/training_args.bin +3 -0
  18. trainig-model/test-model.py +45 -0
  19. trainig-model/training.py +105 -0
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trainig-model/results/checkpoint-2721/training_args.bin ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0a3254a81eb649f988bb79c5c9646a93243fb9159558086f95397c39a7ea1760
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+ size 5112
trainig-model/test-model.py ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
2
+ import torch
3
+
4
+ def test_model(input_text, model, tokenizer, max_length=128):
5
+ # Tokenize a entrada
6
+ inputs = tokenizer(input_text, return_tensors="pt", max_length=max_length, truncation=True, padding="max_length")
7
+
8
+ # Mover para GPU se disponível
9
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
10
+ model.to(device)
11
+ inputs = {k: v.to(device) for k, v in inputs.items()}
12
+
13
+ # Gerar a saída com parâmetros ajustados
14
+ outputs = model.generate(
15
+ **inputs,
16
+ max_length=max_length,
17
+ num_return_sequences=1,
18
+ no_repeat_ngram_size=2,
19
+ temperature=0.7,
20
+ top_k=50,
21
+ top_p=0.95,
22
+ )
23
+
24
+ # Decodificar a saída
25
+ decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
26
+
27
+ return decoded_output
28
+
29
+ # Carregar o modelo e tokenizer salvos
30
+ model_path = "./meu_modelo_treinado"
31
+ model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
32
+ tokenizer = AutoTokenizer.from_pretrained(model_path)
33
+
34
+ # Exemplo de uso
35
+ input_texts = ["workers", "employee", "labor"]
36
+
37
+ for input_text in input_texts:
38
+ output = test_model(input_text, model, tokenizer)
39
+ print(f"Input: {input_text}")
40
+ print(f"Output: {output}")
41
+ print("-" * 30)
42
+
43
+ # Imprimir informações sobre o modelo
44
+ print(f"Tamanho do vocabulário: {len(tokenizer)}")
45
+ print(f"Número de parâmetros do modelo: {model.num_parameters()}")
trainig-model/training.py ADDED
@@ -0,0 +1,105 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ import os
3
+ import psutil
4
+ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Trainer, TrainingArguments, DataCollatorForSeq2Seq
5
+ from datasets import load_dataset
6
+ import torch
7
+
8
+ # Configurar logging
9
+ logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
10
+ logger = logging.getLogger(__name__)
11
+
12
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
13
+ logger.info(f"Usando dispositivo: {device}")
14
+
15
+ def log_gpu_memory():
16
+ if torch.cuda.is_available():
17
+ gpu_memory = torch.cuda.memory_allocated() / 1024 / 1024 # em MB
18
+ logger.info(f"Uso de memória GPU: {gpu_memory:.2f} MB")
19
+ else:
20
+ logger.info("GPU não disponível")
21
+
22
+ # Função para monitorar o uso de RAM
23
+ #def log_memory_usage():
24
+ # process = psutil.Process(os.getpid())
25
+ # mem = process.memory_info().rss / 1024 / 1024 # em MB
26
+ # logger.info(f"Uso de memória atual: {mem:.2f} MB")
27
+
28
+ # Carregue o modelo e o tokenizer
29
+ logger.info("Carregando modelo e tokenizer...")
30
+ model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
31
+ tokenizer = AutoTokenizer.from_pretrained("t5-small")
32
+ log_gpu_memory()
33
+
34
+ # Prepare seu dataset
35
+ logger.info("Carregando dataset...")
36
+ dataset = load_dataset('csv', data_files='../extract-csv/social_keywords.csv')
37
+ log_gpu_memory()
38
+
39
+ # Função para preparar os dados
40
+ def preprocess_function(examples):
41
+ inputs = examples['keyword']
42
+ targets = examples['context']
43
+ model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding='max_length')
44
+
45
+ # Tokenize targets
46
+ with tokenizer.as_target_tokenizer():
47
+ labels = tokenizer(targets, max_length=128, truncation=True, padding='max_length')
48
+
49
+ model_inputs['labels'] = labels['input_ids']
50
+ return model_inputs
51
+
52
+ # Aplicar pré-processamento
53
+ logger.info("Pré-processando dataset...")
54
+ train_dataset = dataset['train'].map(preprocess_function, batched=True, remove_columns=dataset['train'].column_names)
55
+ log_gpu_memory()
56
+
57
+ print("Amostra de dados processados:")
58
+ print(train_dataset[:5])
59
+
60
+ # Configure os argumentos de treinamento
61
+ logger.info("Configurando argumentos de treinamento...")
62
+ training_args = TrainingArguments(
63
+ output_dir="./results",
64
+ num_train_epochs=3,
65
+ per_device_train_batch_size=16,
66
+ save_steps=10_000,
67
+ save_total_limit=2,
68
+ logging_dir='./logs',
69
+ logging_steps=100,
70
+ )
71
+
72
+ # Classe personalizada de Trainer para monitorar o uso de memória
73
+ class MemoryMonitorTrainer(Trainer):
74
+ def training_step(self, model, inputs):
75
+ loss = super().training_step(model, inputs)
76
+ if self.state.global_step % 100 == 0: # Log a cada 100 steps
77
+ log_gpu_memory()
78
+ return loss
79
+
80
+ # Crie um data collator
81
+ data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, padding='max_length', max_length=128)
82
+
83
+ # Inicie o treinamento
84
+ logger.info("Iniciando treinamento...")
85
+ trainer = MemoryMonitorTrainer(
86
+ model=model,
87
+ args=training_args,
88
+ train_dataset=train_dataset,
89
+ tokenizer=tokenizer,
90
+ data_collator=data_collator,
91
+ )
92
+
93
+ trainer.train()
94
+
95
+ logger.info("Treinamento concluído!")
96
+ log_gpu_memory()
97
+
98
+ # Salvar o modelo treinado
99
+ output_dir = "./meu_modelo_treinado"
100
+ logger.info(f"Salvando o modelo treinado em {output_dir}")
101
+ model.save_pretrained(output_dir)
102
+ tokenizer.save_pretrained(output_dir)
103
+ log_gpu_memory()
104
+
105
+ logger.info("Processo finalizado!")