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fine-tuned-distilbert-autofill

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@@ -18,29 +18,12 @@ should probably proofread and complete it, then remove this comment. -->
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  # fine-tuned-distilbert-autofill
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- This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the None dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.2367
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  - Precision: 0.9484
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  - Recall: 0.9473
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  - F1: 0.9473
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- - Confusion Matrix: [[ 94 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
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- [ 14 44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
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- [ 0 0 130 0 0 0 0 0 0 0 0 0 0 0 0 0 9]
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- [ 0 0 0 33 0 0 0 0 0 0 0 0 0 0 2 0 0]
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- [ 0 0 2 0 64 0 0 0 0 0 3 0 0 0 0 0 7]
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- [ 0 0 0 0 0 53 0 0 0 0 0 0 0 0 2 0 0]
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- [ 0 0 0 0 0 0 37 1 0 0 0 0 0 0 0 0 3]
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- [ 0 0 0 0 0 0 4 35 0 0 0 0 0 0 0 0 2]
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- [ 1 0 0 0 0 1 0 0 43 0 0 0 0 0 2 0 0]
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- [ 0 0 0 0 0 0 0 0 0 31 0 0 0 0 1 0 0]
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- [ 0 0 0 0 2 0 0 2 0 0 10 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 1 0 0 1 16 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
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- [ 0 0 0 1 0 0 1 0 0 0 0 0 0 0 73 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
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- [ 1 0 9 1 4 0 0 0 2 0 2 0 1 2 1 0 977]]
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  ## Model description
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@@ -69,94 +52,6 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Confusion Matrix |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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- | 0.7726 | 1.0 | 987 | 0.3096 | 0.8920 | 0.9141 | 0.8988 | [[100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
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- [ 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
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- [ 0 0 129 0 0 0 0 0 0 0 0 0 0 0 0 0 10]
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- [ 0 0 0 32 0 0 0 0 1 1 0 0 0 0 0 0 1]
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- [ 0 0 4 0 63 0 0 0 0 0 0 0 0 0 0 0 9]
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- [ 0 0 0 0 0 52 0 0 0 2 0 0 0 0 0 0 1]
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- [ 0 0 0 0 0 0 36 0 0 0 2 0 0 0 0 0 3]
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- [ 0 0 0 0 0 0 2 33 0 0 4 0 0 0 0 0 2]
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- [ 1 0 0 0 0 1 0 0 43 2 0 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 32 0 0 0 0 0 0 0]
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- [ 0 0 0 0 2 0 0 0 0 0 12 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 4 0 0 0 1 13 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 1 0 1 2 0 0 0 0 71 0 0]
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- [ 0 0 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0]
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- [ 1 0 7 1 5 0 0 0 1 3 1 0 0 0 1 0 980]] |
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- | 0.2616 | 2.0 | 1974 | 0.2645 | 0.9356 | 0.9273 | 0.9179 | [[ 99 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
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- [ 43 7 5 0 0 0 0 0 0 0 0 0 0 0 0 0 3]
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- [ 0 0 128 0 0 0 0 0 0 0 0 0 0 0 0 0 11]
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- [ 0 0 0 33 0 0 0 0 1 0 0 0 0 0 0 0 1]
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- [ 0 0 0 0 64 0 0 0 0 0 0 0 0 0 0 0 12]
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- [ 0 0 0 1 0 53 0 0 0 0 0 0 0 0 0 0 1]
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- [ 0 0 0 0 0 0 36 2 0 0 0 0 0 0 0 0 3]
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- [ 0 0 0 0 0 0 3 36 0 0 0 0 0 0 0 0 2]
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- [ 1 0 0 0 0 2 0 0 43 0 0 0 0 0 0 0 1]
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- [ 0 0 0 1 0 0 0 0 0 31 0 0 0 0 0 0 0]
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- [ 0 0 0 0 2 0 0 3 0 0 9 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 1 3 0 0 1 13 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
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- [ 0 0 0 1 0 0 1 0 1 1 0 0 0 0 71 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
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- [ 1 0 6 1 3 0 0 0 1 0 2 0 1 2 1 0 982]] |
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- | 0.1814 | 3.0 | 2961 | 0.2332 | 0.9437 | 0.9422 | 0.9420 | [[ 94 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
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- [ 15 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
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- [ 0 0 127 0 1 0 0 0 0 0 0 0 0 0 0 0 11]
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- [ 0 0 0 34 0 0 0 0 0 0 0 0 0 0 0 0 1]
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- [ 0 0 1 0 63 0 0 0 0 0 2 0 0 0 0 0 10]
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- [ 0 0 0 1 0 52 0 0 0 1 1 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 37 1 0 0 0 0 0 0 0 0 3]
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- [ 0 0 0 0 0 0 4 35 0 0 0 0 0 0 0 0 2]
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- [ 1 0 0 0 0 1 0 0 43 2 0 0 0 0 0 0 0]
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- [ 0 0 0 1 0 0 0 0 0 31 0 0 0 0 0 0 0]
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- [ 0 0 0 0 2 0 0 2 0 0 10 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 2 2 0 0 1 13 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
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- [ 0 0 0 1 0 0 1 0 0 1 0 0 1 0 71 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
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- [ 1 0 8 1 4 0 0 1 1 0 2 0 1 2 1 0 978]] |
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- | 0.1248 | 4.0 | 3948 | 0.2255 | 0.9501 | 0.9479 | 0.9482 | [[ 95 4 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
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- [ 13 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
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- [ 0 0 130 0 0 0 0 0 0 0 0 0 0 0 0 0 9]
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- [ 0 0 0 33 0 0 0 0 0 0 0 0 0 0 2 0 0]
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- [ 0 0 2 0 65 0 0 0 0 0 4 0 0 0 0 0 5]
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- [ 0 0 0 0 0 52 0 0 0 0 1 0 0 0 2 0 0]
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- [ 0 0 0 0 0 0 38 0 0 0 0 0 0 0 0 0 3]
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- [ 0 0 0 0 0 0 5 34 0 0 0 0 0 0 0 0 2]
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- [ 1 0 0 0 0 1 0 0 43 0 0 0 0 0 2 0 0]
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- [ 0 0 0 0 0 0 0 0 1 30 0 0 0 0 1 0 0]
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- [ 0 0 0 0 2 0 2 0 0 0 10 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 1 0 0 1 16 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
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- [ 0 0 0 1 0 0 1 0 0 0 0 0 0 0 73 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
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- [ 1 0 9 1 4 0 0 0 2 0 2 0 1 2 1 0 977]] |
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- | 0.1032 | 5.0 | 4935 | 0.2367 | 0.9484 | 0.9473 | 0.9473 | [[ 94 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]
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- [ 14 44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
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- [ 0 0 130 0 0 0 0 0 0 0 0 0 0 0 0 0 9]
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- [ 0 0 0 33 0 0 0 0 0 0 0 0 0 0 2 0 0]
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- [ 0 0 2 0 64 0 0 0 0 0 3 0 0 0 0 0 7]
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- [ 0 0 0 0 0 53 0 0 0 0 0 0 0 0 2 0 0]
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- [ 0 0 0 0 0 0 37 1 0 0 0 0 0 0 0 0 3]
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- [ 0 0 0 0 0 0 4 35 0 0 0 0 0 0 0 0 2]
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- [ 1 0 0 0 0 1 0 0 43 0 0 0 0 0 2 0 0]
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- [ 0 0 0 0 0 0 0 0 0 31 0 0 0 0 1 0 0]
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- [ 0 0 0 0 2 0 0 2 0 0 10 0 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 1 0 0 1 16 0 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0]
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- [ 0 0 0 1 0 0 1 0 0 0 0 0 0 0 73 0 0]
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- [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0]
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- [ 1 0 9 1 4 0 0 0 2 0 2 0 1 2 1 0 977]] |
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-
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  ### Framework versions
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  # fine-tuned-distilbert-autofill
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+ This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.2367
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  - Precision: 0.9484
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  - Recall: 0.9473
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  - F1: 0.9473
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model description
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  ### Training results
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  ### Framework versions
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