Error Rate and Accuracy Sentiment Specific Embeddings about deepnl HOT 3 CLOSED

Correct.

from deepnl.

Don't worry about those numbers.
You shoud get useable embeddings anyway.

On 29/4/2016 7:54, maulikdang wrote:

I am using dl-sentiwords.py with the word2vec variant. (word2vec
vector file downloaded from GloVe).

The training runs successfully but the error increases with every
epoch iteration and the accuracy remains at 0.00. Any inputs on
what could be done to fix the issue?

maulik@maulik-VPCEH38FN:~/deepnl/bin$ python dl-sentiwords.py
training.tsv --vectors vectors.txt --variant word2vec --vocab
vocab.txt --model model1 -e 30 --hidden 20
Saving vocabulary in vocab.txt
Creating new network...
... with the following parameters:

|Input layer size: 550 Hidden layer size: 20 Output size: 2 |

Starting training
Epoch: 0, pairs: 10000, sent: 52656, avg. error: 4.176
1 epochs Examples: 10041 Error: 62460.347864 Accuracy: 0.000000 6925
corrections skipped
Epoch: 1, pairs: 20000, sent: 52337, avg. error: 8.991
2 epochs Examples: 20082 Error: 207734.402282 Accuracy: 0.000000 7105
corrections skipped
Epoch: 2, pairs: 30000, sent: 52020, avg. error: 14.748
3 epochs Examples: 30123 Error: 388989.344357 Accuracy: 0.000000 7052
corrections skipped
Epoch: 3, pairs: 40000, sent: 51791, avg. error: 20.649
4 epochs Examples: 40164 Error: 573208.304635 Accuracy: 0.000000 6956
corrections skipped
Epoch: 4, pairs: 50000, sent: 51598, avg. error: 26.750
5 epochs Examples: 50205 Error: 767234.504043 Accuracy: 0.000000 6906
corrections skipped
Epoch: 5, pairs: 60000, sent: 51239, avg. error: 32.770
6 epochs Examples: 60246 Error: 951057.397979 Accuracy: 0.000000 6981
corrections skipped
Epoch: 6, pairs: 70000, sent: 51166, avg. error: 38.861
7 epochs Examples: 70287 Error: 1129657.364728 Accuracy: 0.000000 7030
corrections skipped
Epoch: 7, pairs: 80000, sent: 50936, avg. error: 45.031
8 epochs Examples: 80328 Error: 1316129.222171 Accuracy: 0.000000 6920
corrections skipped
Epoch: 8, pairs: 90000, sent: 50857, avg. error: 51.284
9 epochs Examples: 90369 Error: 1530057.560660 Accuracy: 0.000000 6999
corrections skipped
Epoch: 9, pairs: 100000, sent: 50718, avg. error: 57.600
10 epochs Examples: 100410 Error: 1705694.047717 Accuracy: 0.000000
7016 corrections skipped
Epoch: 10, pairs: 110000, sent: 50425, avg. error: 63.825
11 epochs Examples: 110451 Error: 1882818.481616 Accuracy: 0.000000
7110 corrections skipped
Epoch: 11, pairs: 120000, sent: 50216, avg. error: 69.894
12 epochs Examples: 120492 Error: 2074675.285961 Accuracy: 0.000000
7015 corrections skipped
Epoch: 12, pairs: 130000, sent: 49730, avg. error: 76.021
13 epochs Examples: 130533 Error: 2265213.329245 Accuracy: 0.000000
7052 corrections skipped
Epoch: 13, pairs: 140000, sent: 49498, avg. error: 82.320
14 epochs Examples: 140574 Error: 2463388.976950 Accuracy: 0.000000
7122 corrections skipped
Epoch: 14, pairs: 150000, sent: 49306, avg. error: 88.331
15 epochs Examples: 150615 Error: 2622099.455269 Accuracy: 0.000000
7068 corrections skipped
Epoch: 15, pairs: 160000, sent: 49051, avg. error: 94.802
16 epochs Examples: 160656 Error: 2853079.937031 Accuracy: 0.000000
7071 corrections skipped
Epoch: 16, pairs: 170000, sent: 48561, avg. error: 101.018
17 epochs Examples: 170697 Error: 2995733.476832 Accuracy: 0.000000
7057 corrections skipped
Epoch: 17, pairs: 180000, sent: 48292, avg. error: 107.027
18 epochs Examples: 180738 Error: 3206937.575615 Accuracy: 0.000000
6999 corrections skipped
Epoch: 18, pairs: 190000, sent: 48281, avg. error: 113.053
19 epochs Examples: 190779 Error: 3404130.852323 Accuracy: 0.000000
7008 corrections skipped
Epoch: 19, pairs: 200000, sent: 47593, avg. error: 119.447
20 epochs Examples: 200820 Error: 3593241.777677 Accuracy: 0.000000
7039 corrections skipped
Epoch: 20, pairs: 210000, sent: 47402, avg. error: 126.057
21 epochs Examples: 210861 Error: 3837913.673458 Accuracy: 0.000000
7039 corrections skipped
Epoch: 21, pairs: 220000, sent: 47181, avg. error: 132.405
22 epochs Examples: 220902 Error: 3961487.942201 Accuracy: 0.000000
7049 corrections skipped
Epoch: 22, pairs: 230000, sent: 47081, avg. error: 138.319
23 epochs Examples: 230943 Error: 4089176.958714 Accuracy: 0.000000
7027 corrections skipped
Epoch: 23, pairs: 240000, sent: 46938, avg. error: 144.327
24 epochs Examples: 240984 Error: 4287874.864616 Accuracy: 0.000000
7095 corrections skipped
Epoch: 24, pairs: 250000, sent: 46900, avg. error: 150.805
25 epochs Examples: 251025 Error: 4578433.858292 Accuracy: 0.000000
7048 corrections skipped
Epoch: 25, pairs: 260000, sent: 46743, avg. error: 156.881
26 epochs Examples: 261066 Error: 4756786.779839 Accuracy: 0.000000
6964 corrections skipped
Epoch: 26, pairs: 270000, sent: 46546, avg. error: 162.995
27 epochs Examples: 271107 Error: 4879088.927508 Accuracy: 0.000000
6984 corrections skipped
Epoch: 27, pairs: 280000, sent: 46313, avg. error: 169.356
28 epochs Examples: 281148 Error: 5061753.620850 Accuracy: 0.000000
7013 corrections skipped
Epoch: 28, pairs: 290000, sent: 46215, avg. error: 175.712
29 epochs Examples: 291189 Error: 5285739.418424 Accuracy: 0.000000
7078 corrections skipped
Epoch: 29, pairs: 300000, sent: 45871, avg. error: 181.846
30 epochs Examples: 301230 Error: 5465347.502520 Accuracy: 0.000000
7121 corrections skipped
Overriding vectors to vectors.txt
Saving trained model to model1

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#32

from deepnl.

okay sir, thank you.
That means after the training i will get sentiment specific embeddings(despite the error rate) that can be used directly?

from deepnl.