Support FARM on Windows OS

examples/doc_classification.py

@@ -1,5 +1,6 @@
 # fmt: off
 import logging
+from pathlib import Path
 
 from farm.data_handler.data_silo import DataSilo
 from farm.data_handler.processor import TextClassificationProcessor
@@ -12,100 +13,107 @@
 from farm.train import Trainer
 from farm.utils import set_all_seeds, MLFlowLogger, initialize_device_settings
 
-logging.basicConfig(
-    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-    datefmt="%m/%d/%Y %H:%M:%S",
-    level=logging.INFO)
-
-ml_logger = MLFlowLogger(tracking_uri="https://public-mlflow.deepset.ai/")
-ml_logger.init_experiment(experiment_name="Public_FARM", run_name="Run_doc_classification")
-
-##########################
-########## Settings
-##########################
-set_all_seeds(seed=42)
-n_epochs = 1
-batch_size = 32
-evaluate_every = 100
-lang_model = "bert-base-german-cased"
-use_amp = None
-
-device, n_gpu = initialize_device_settings(use_cuda=True, use_amp=use_amp)
-
-# 1.Create a tokenizer
-tokenizer = Tokenizer.load(
-    pretrained_model_name_or_path=lang_model,
-    do_lower_case=False)
-
-# 2. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset
-# Here we load GermEval 2018 Data.
-
-label_list = ["OTHER", "OFFENSE"]
-metric = "f1_macro"
-
-processor = TextClassificationProcessor(tokenizer=tokenizer,
-                                        max_seq_len=128,
-                                        data_dir="../data/germeval18",
-                                        label_list=label_list,
-                                        metric=metric,
-                                        label_column_name="coarse_label"
-                                        )
-
-# 3. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them and calculates a few descriptive statistics of our datasets
-data_silo = DataSilo(
-    processor=processor,
-    batch_size=batch_size)
-
-# 4. Create an AdaptiveModel
-# a) which consists of a pretrained language model as a basis
-language_model = LanguageModel.load(lang_model)
-# b) and a prediction head on top that is suited for our task => Text classification
-prediction_head = TextClassificationHead(layer_dims=[768, len(processor.tasks["text_classification"]["label_list"])],
-                                         class_weights=data_silo.calculate_class_weights(task_name="text_classification"))
-
-
-
-model = AdaptiveModel(
-    language_model=language_model,
-    prediction_heads=[prediction_head],
-    embeds_dropout_prob=0.1,
-    lm_output_types=["per_sequence"],
-    device=device)
-
-# 5. Create an optimizer
-model, optimizer, lr_schedule = initialize_optimizer(
-    model=model,
-    learning_rate=2e-5,
-    device=device,
-    n_batches=len(data_silo.loaders["train"]),
-    n_epochs=n_epochs,
-    use_amp=use_amp)
-
-# 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time
-trainer = Trainer(
-    optimizer=optimizer,
-    data_silo=data_silo,
-    epochs=n_epochs,
-    n_gpu=n_gpu,
-    lr_schedule=lr_schedule,
-    evaluate_every=evaluate_every,
-    device=device,)
-
-# 7. Let it grow
-model = trainer.train(model)
-
-# 8. Hooray! You have a model. Store it:
-save_dir = "saved_models/bert-german-doc-tutorial"
-model.save(save_dir)
-processor.save(save_dir)
-
-# 9. Load it & harvest your fruits (Inference)
-basic_texts = [
-    {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"},
-    {"text": "Martin Müller spielt Handball in Berlin"},
-]
-model = Inferencer.load(save_dir)
-result = model.inference_from_dicts(dicts=basic_texts)
-print(result)
+
+def doc_classifcation():
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO)
+
+    ml_logger = MLFlowLogger(tracking_uri="https://public-mlflow.deepset.ai/")
+    ml_logger.init_experiment(experiment_name="Public_FARM", run_name="Run_doc_classification")
+
+    ##########################
+    ########## Settings
+    ##########################
+    set_all_seeds(seed=42)
+    device, n_gpu = initialize_device_settings(use_cuda=True)
+    n_epochs = 1
+    batch_size = 32
+    evaluate_every = 100
+    lang_model = "bert-base-german-cased"
+    use_amp = None
+
+    device, n_gpu = initialize_device_settings(use_cuda=True, use_amp=use_amp)
+
+    # 1.Create a tokenizer
+    tokenizer = Tokenizer.load(
+        pretrained_model_name_or_path=lang_model,
+        do_lower_case=False)
+
+    # 2. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset
+    # Here we load GermEval 2018 Data.
+
+    label_list = ["OTHER", "OFFENSE"]
+    metric = "f1_macro"
+
+    processor = TextClassificationProcessor(tokenizer=tokenizer,
+                                            max_seq_len=128,
+                                            data_dir=Path("../data/germeval18"),
+                                            label_list=label_list,
+                                            metric=metric,
+                                            label_column_name="coarse_label"
+                                            )
+
+    # 3. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them and calculates a
+    #    few descriptive statistics of our datasets
+    data_silo = DataSilo(
+        processor=processor,
+        batch_size=batch_size)
+
+    # 4. Create an AdaptiveModel
+    # a) which consists of a pretrained language model as a basis
+    language_model = LanguageModel.load(lang_model)
+    # b) and a prediction head on top that is suited for our task => Text classification
+    prediction_head = TextClassificationHead(
+        layer_dims=[768, len(processor.tasks["text_classification"]["label_list"])],
+        class_weights=data_silo.calculate_class_weights(task_name="text_classification"))
+
+    model = AdaptiveModel(
+        language_model=language_model,
+        prediction_heads=[prediction_head],
+        embeds_dropout_prob=0.1,
+        lm_output_types=["per_sequence"],
+        device=device)
+
+    # 5. Create an optimizer
+    model, optimizer, lr_schedule = initialize_optimizer(
+        model=model,
+        learning_rate=2e-5,
+        device=device,
+        n_batches=len(data_silo.loaders["train"]),
+        n_epochs=n_epochs,
+        use_amp=use_amp)
+
+    # 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time
+    trainer = Trainer(
+        optimizer=optimizer,
+        data_silo=data_silo,
+        epochs=n_epochs,
+        n_gpu=n_gpu,
+        lr_schedule=lr_schedule,
+        evaluate_every=evaluate_every,
+        device=device)
+
+    # 7. Let it grow
+    model = trainer.train(model)
+
+    # 8. Hooray! You have a model. Store it:
+    save_dir = Path("saved_models/bert-german-doc-tutorial")
+    model.save(save_dir)
+    processor.save(save_dir)
+
+    # 9. Load it & harvest your fruits (Inference)
+    basic_texts = [
+        {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"},
+        {"text": "Martin Müller spielt Handball in Berlin"},
+    ]
+    model = Inferencer.load(save_dir)
+    result = model.inference_from_dicts(dicts=basic_texts)
+    print(result)
+
+
+if __name__ == "__main__":
+    doc_classifcation()
 
 # fmt: on