predictions are all zeros

[ama454]

When I try to run the code every prediction is all 0 or all 1. I've tried re-balancing my dataset, but it didn't work for me,

[Omar Mahhmoud]

would you share your notebook to figure out what's the problem?

[ama454]

`import librosa
from sklearn.metrics import classification_report

def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["name"])
speech_array = speech_array
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
speech_array = resampler(speech_array).squeeze().numpy()

batch["speech"] = speech_array
return batch

def predict(batch):
features = processor(batch["speech"], sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt", padding=True)

input_values = features.input_values.to(device)

with torch.no_grad():
    logits = model(input_values).logits 

pred_ids = torch.argmax(logits, dim=-1).detach().cpu().numpy()
batch["predicted"] = pred_ids
return batch

if name == 'main':

data_files = {
    "test" : 'test.csv'
}
test_dataset = load_dataset('csv', data_files = data_files, delimiter = "\t")["test"]
print(test_dataset)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {device}")


test_dataset = test_dataset.map(speech_file_to_array_fn)

result = test_dataset.map(predict,batch_size=8)

label_names = [config.id2label[i] for i in range(config.num_labels)]

print(f'Labels: {label_names}')
      
y_true = [config.label2id[name] for name in result["emotion"]]
y_pred = result["predicted"]

print(y_true)
print(y_pred)
`

Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-a94fa44ad00c5272/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317... Downloading data files: 100% 1/1 [00:00<00:00, 29.44it/s] Extracting data files: 100% 1/1 [00:00<00:00, 33.98it/s] Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-a94fa44ad00c5272/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data. 100% 1/1 [00:00<00:00, 27.50it/s] Dataset({ features: ['name', 'emotion'], num_rows: 30 }) Device: cpu 100% 30/30 [00:01<00:00, 27.23ex/s] 100% 30/30 [15:19<00:00, 66.05s/ex] Labels: [0, 1] [1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0] [[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]]

print(classification_report(y_true,y_pred))
` precision recall f1-score support

       0       0.00      0.00      0.00        14
       1       0.53      1.00      0.70        16

accuracy                           0.53        30

macro avg 0.27 0.50 0.35 30
weighted avg 0.28 0.53 0.37 30

/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
`