# Results: FastText
```
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/cd128674518a40f188195fed86512184'
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/ac0e2c59ee944c07af0f7d5b6ad9ffd1'
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/7b21e9fb6ee64d4bb7d210a47784c094'
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/053a11ae86a446ffaee8fa7f11dc81df'
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Build finished in 0.30 sec.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Build finished in 0.34 sec.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Build finished in 0.28 sec.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Build finished in 0.28 sec.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Training finished in 312.29 sec.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:  Validation loss: -0.883
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.883
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:  Training loss: 0.329469
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/be56fe65ba39471d83df1cbc6a41a11b'
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Build finished in 0.33 sec.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Training finished in 312.65 sec.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:  Validation loss: -0.888
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.888
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:  Training loss: 0.31366
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/c57044730473433d82cdc342918a5606'
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Build finished in 0.28 sec.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Training finished in 312.95 sec.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:  Validation loss: -0.897
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.897
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:  Training loss: 0.277508
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/de53d34b14f74c66abe97283f6c8d92c'
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Build finished in 0.30 sec.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Training finished in 316.22 sec.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:  Validation loss: -0.8976
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.8976
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:  Training loss: 0.257975
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/292548946e204b6e9859f79d718634df'
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Build finished in 0.45 sec.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Starting training.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:Training finished in 319.10 sec.
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:  Validation loss: -0.888
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.888
[2m[36m(pid=3754)[0m INFO:gobbli.experiment.base:  Training loss: 0.362867
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:Training finished in 319.25 sec.
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:  Validation loss: -0.8912
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.8912
[2m[36m(pid=3753)[0m INFO:gobbli.experiment.base:  Training loss: 0.309882
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:Training finished in 324.26 sec.
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:  Validation loss: -0.8998
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.8998
[2m[36m(pid=3752)[0m INFO:gobbli.experiment.base:  Training loss: 0.312032
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Training finished in 321.45 sec.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:RESULTS:
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:  Validation loss: -0.9004
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:  Validation accuracy: 0.9004
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:  Training loss: 0.29699
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:FastText initialized with data directory '/data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/9a7abdc6832743269eb34cd36db34aa3'
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Starting build.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Build finished in 0.30 sec.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Starting prediction.
[2m[36m(pid=3751)[0m INFO:gobbli.experiment.base:Prediction finished in 5.15 sec.
[2m[36m(pid=3751)[0m /usr/local/lib/python3.7/site-packages/ray/pyarrow_files/pyarrow/serialization.py:165: FutureWarning: The SparseDataFrame class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version
[2m[36m(pid=3751)[0m   if isinstance(obj, pd.SparseDataFrame):

```
|    |   valid_loss |   valid_accuracy |   train_loss | multilabel   | labels         | checkpoint                                                                                                                                             | node_ip_address   | model_params                              |
|---:|-------------:|-----------------:|-------------:|:-------------|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|:------------------------------------------|
|  0 |      -0.883  |           0.883  |     0.329469 | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/cd128674518a40f188195fed86512184/train/e68480643e744624978ffd5a4bf373f7/output/model | 172.80.10.2       | {'dim': 100, 'lr': 0.5, 'word_ngrams': 1} |
|  1 |      -0.8976 |           0.8976 |     0.257975 | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/ac0e2c59ee944c07af0f7d5b6ad9ffd1/train/8c0b4c217c4441c98446023e3aee5a15/output/model | 172.80.10.2       | {'dim': 100, 'lr': 0.5, 'word_ngrams': 2} |
|  2 |      -0.888  |           0.888  |     0.31366  | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/7b21e9fb6ee64d4bb7d210a47784c094/train/540bf38999424506a1619c0da46d80e8/output/model | 172.80.10.2       | {'dim': 100, 'lr': 1.0, 'word_ngrams': 1} |
|  3 |      -0.897  |           0.897  |     0.277508 | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/053a11ae86a446ffaee8fa7f11dc81df/train/25b72bd83196489e891c3bc204e976e9/output/model | 172.80.10.2       | {'dim': 100, 'lr': 1.0, 'word_ngrams': 2} |
|  4 |      -0.888  |           0.888  |     0.362867 | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/be56fe65ba39471d83df1cbc6a41a11b/train/a2e43f09612642e7a039fc4f9d9f39f6/output/model | 172.80.10.2       | {'dim': 300, 'lr': 0.5, 'word_ngrams': 1} |
|  5 |      -0.8998 |           0.8998 |     0.312032 | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/c57044730473433d82cdc342918a5606/train/364f850e9b7f41258710ec9cfb508768/output/model | 172.80.10.2       | {'dim': 300, 'lr': 0.5, 'word_ngrams': 2} |
|  6 |      -0.8912 |           0.8912 |     0.309882 | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/de53d34b14f74c66abe97283f6c8d92c/train/839eb49b93384153be401e693b9253a2/output/model | 172.80.10.2       | {'dim': 300, 'lr': 1.0, 'word_ngrams': 1} |
|  7 |      -0.9004 |           0.9004 |     0.29699  | False        | ['neg', 'pos'] | /data/users/jnance/gobbli/benchmark/benchmark_data/model/FastText/292548946e204b6e9859f79d718634df/train/f940017802dd457bbfc6ecf58e62e397/output/model | 172.80.10.2       | {'dim': 300, 'lr': 1.0, 'word_ngrams': 2} |
```
Metrics:
--------
Weighted F1 Score: 0.8860921370432054
Weighted Precision Score: 0.8864981646458191
Weighted Recall Score: 0.88612
Accuracy: 0.88612

Classification Report:
----------------------
              precision    recall  f1-score   support

         neg       0.00      0.00      0.00         0
         pos       0.00      0.00      0.00         0

   micro avg       0.00      0.00      0.00         0
   macro avg       0.00      0.00      0.00         0
weighted avg       0.00      0.00      0.00         0


```

![Results](FastText/plot.png)
---