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full training

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mm 2023-05-04 10:15:17 +00:00
parent c7b294c557
commit fab8952d59
1 changed files with 3 additions and 3 deletions
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train.py
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@ -32,7 +32,7 @@ model = SentenceTransformer(model_name, device="cuda")
# (fake.city(), fake.city(), np.random.rand()) # (fake.city(), fake.city(), np.random.rand())
# for _ in range(num_examples) # for _ in range(num_examples)
# ] # ]
data = pd.read_csv("city_distances_sample.csv") data = pd.read_csv("city_distances_full.csv")
MAX_DISTANCE = 20_037.5 # global max distance MAX_DISTANCE = 20_037.5 # global max distance
# MAX_DISTANCE = data["distance"].max() # about 5k # MAX_DISTANCE = data["distance"].max() # about 5k
@ -70,7 +70,7 @@ print("TRAINING")
training_args = { training_args = {
"output_path": "./output", "output_path": "./output",
# "evaluation_steps": steps_per_epoch, # already evaluates at the end of each epoch # "evaluation_steps": steps_per_epoch, # already evaluates at the end of each epoch
"epochs": 5, "epochs": 20,
"warmup_steps": 500, "warmup_steps": 500,
"optimizer_params": {"lr": 2e-5}, "optimizer_params": {"lr": 2e-5},
# "weight_decay": 0, # not sure if this helps but works fine without setting it. # "weight_decay": 0, # not sure if this helps but works fine without setting it.
@ -78,7 +78,7 @@ training_args = {
"save_best_model": True, "save_best_model": True,
"checkpoint_path": "./checkpoints_absmax_split", "checkpoint_path": "./checkpoints_absmax_split",
"checkpoint_save_steps": steps_per_epoch, "checkpoint_save_steps": steps_per_epoch,
"checkpoint_save_total_limit": 20, "checkpoint_save_total_limit": 100,
} }
print(f"TRAINING ARGUMENTS:\n {training_args}") print(f"TRAINING ARGUMENTS:\n {training_args}")