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Validation Loss and Accuracy report:

  • Validation Loss: 0.11179830832788
  • Validation Accuracy: 0.9352647152068487

Classification Report

Precision Recall F1-Score Support
commodity 0.78 0.73 0.75 86
company 0.76 0.80 0.78 230
delivery_location 0.65 0.41 0.50 32
delivery_port 0.69 0.89 0.78 309
delivery_state 0.71 0.63 0.67 82
incoterms 0.77 0.88 0.82 117
measures 0.77 0.84 0.80 629
package_type 0.95 0.94 0.95 286
pickup_cap 0.86 0.93 0.90 107
pickup_location 0.71 0.77 0.74 356
pickup_port 0.45 0.42 0.43 12
pickup_state 0.68 0.75 0.71 71
quantity 0.78 0.91 0.84 154
stackable 0.94 0.98 0.96 61
total_quantity 0.86 0.60 0.71 10
total_volume 0.86 0.46 0.60 13
total_weight 0.68 0.81 0.74 136
volume 0.60 0.72 0.65 43
weight 0.67 0.58 0.62 114
Micro Avg 0.76 0.82 0.79 2848
Macro Avg 0.75 0.74 0.73 2848
Weighted Avg 0.76 0.82 0.79 2848

Model Details

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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  • Language(s) (NLP): Italian/English
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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