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challenging-america-geo-prediction
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roberta base with regression layer on top lr=1e-8 4 epochs

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kubapok 2021-09-15 10:37:26 +02:00
parent 8a5bc51f44
commit 10f548680a
3 changed files with 11610 additions and 11609 deletions
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12488
dev-0/out.tsv
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9
roberta_regressor_head/train.py
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@ -65,7 +65,7 @@ class RegressorHead(torch.nn.Module):
regressor_head = RegressorHead().to(device)
optimizer = torch.optim.Adam(list(roberta.parameters()) + list(regressor_head.parameters()), lr=1e-6)
optimizer = torch.optim.Adam(list(roberta.parameters()) + list(regressor_head.parameters()), lr=1e-8)
criterion = torch.nn.MSELoss(reduction='sum').to(device)
BATCH_SIZE = 1
@ -118,7 +118,8 @@ def eval_short():
loss = 0.0
loss_clipped = 0.0
loss_scaled = 0.0
for batch, year in tqdm(get_train_batch(dev_in[:1000],dev_year_scaled[:1000])):
eval_num = 10000
for batch, year in tqdm(get_train_batch(dev_in[:eval_num],dev_year_scaled[:eval_num])):
x = regressor_head(batch.to(device)).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
@ -130,8 +131,8 @@ def eval_short():
loss_scaled += criterion_eval(x, year).item()
loss += criterion_eval(original_x, original_year).item()
loss_clipped += criterion_eval(original_x_clipped, original_year).item()
print('valid loss scaled: ' + str(np.sqrt(loss_scaled/1000)))
print('valid loss: ' + str(np.sqrt(loss/1000)))
print('valid loss scaled: ' + str(np.sqrt(loss_scaled/eval_num)))
print('valid loss: ' + str(np.sqrt(loss/eval_num)))
print('valid loss clipped: ' + str(np.sqrt(loss_clipped/len(dev_year))))

10722
test-A/out.tsv
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