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Text Classification
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Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
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Training complete

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  1. README.md +27 -30
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -6,8 +6,6 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
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- - precision
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- - recall
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  - f1
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  model-index:
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  - name: results
@@ -21,11 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1795
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- - Accuracy: 0.9418
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- - Precision: 0.9421
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- - Recall: 0.9418
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- - F1: 0.9419
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  ## Model description
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@@ -52,33 +48,34 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 16
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 3
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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- |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.4176 | 0.0667 | 500 | 0.3007 | 0.9030 | 0.9031 | 0.9030 | 0.9027 |
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- | 0.265 | 0.1333 | 1000 | 0.2526 | 0.9161 | 0.9161 | 0.9161 | 0.9159 |
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- | 0.2408 | 0.2 | 1500 | 0.2476 | 0.9226 | 0.9245 | 0.9226 | 0.9224 |
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- | 0.231 | 0.2667 | 2000 | 0.2341 | 0.9297 | 0.9311 | 0.9297 | 0.9295 |
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- | 0.2335 | 0.3333 | 2500 | 0.2143 | 0.9297 | 0.9296 | 0.9297 | 0.9297 |
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- | 0.2417 | 0.4 | 3000 | 0.2000 | 0.9325 | 0.9338 | 0.9325 | 0.9327 |
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- | 0.208 | 0.4667 | 3500 | 0.2060 | 0.9314 | 0.9323 | 0.9314 | 0.9315 |
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- | 0.2133 | 0.5333 | 4000 | 0.2011 | 0.9346 | 0.9349 | 0.9346 | 0.9347 |
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- | 0.2042 | 0.6 | 4500 | 0.2035 | 0.9345 | 0.9366 | 0.9345 | 0.9346 |
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- | 0.1947 | 0.6667 | 5000 | 0.1945 | 0.9392 | 0.9392 | 0.9392 | 0.9392 |
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- | 0.1954 | 0.7333 | 5500 | 0.1993 | 0.9367 | 0.9388 | 0.9367 | 0.9368 |
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- | 0.1958 | 0.8 | 6000 | 0.2001 | 0.9411 | 0.9414 | 0.9411 | 0.9412 |
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- | 0.2002 | 0.8667 | 6500 | 0.1906 | 0.9397 | 0.9403 | 0.9397 | 0.9399 |
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- | 0.1885 | 0.9333 | 7000 | 0.1898 | 0.9420 | 0.9422 | 0.9420 | 0.9420 |
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- | 0.1979 | 1.0 | 7500 | 0.1795 | 0.9418 | 0.9421 | 0.9418 | 0.9419 |
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- | 0.1492 | 1.0667 | 8000 | 0.1933 | 0.9459 | 0.9460 | 0.9459 | 0.9459 |
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- | 0.148 | 1.1333 | 8500 | 0.1978 | 0.9426 | 0.9437 | 0.9426 | 0.9427 |
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- | 0.137 | 1.2 | 9000 | 0.1941 | 0.9466 | 0.9469 | 0.9466 | 0.9467 |
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- | 0.1433 | 1.2667 | 9500 | 0.1988 | 0.9453 | 0.9457 | 0.9453 | 0.9453 |
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- | 0.1403 | 1.3333 | 10000 | 0.1926 | 0.9441 | 0.9448 | 0.9441 | 0.9441 |
 
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
 
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  - f1
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  model-index:
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  - name: results
 
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  This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1979
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+ - Accuracy: 0.9434
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+ - F1: 0.9435
 
 
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  ## Model description
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  - total_train_batch_size: 16
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 2
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
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+ | 0.4206 | 0.0667 | 500 | 0.3065 | 0.9021 | 0.9019 |
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+ | 0.2721 | 0.1333 | 1000 | 0.2626 | 0.9151 | 0.9149 |
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+ | 0.2403 | 0.2 | 1500 | 0.2584 | 0.9192 | 0.9190 |
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+ | 0.2314 | 0.2667 | 2000 | 0.2398 | 0.9267 | 0.9263 |
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+ | 0.232 | 0.3333 | 2500 | 0.2190 | 0.9318 | 0.9319 |
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+ | 0.246 | 0.4 | 3000 | 0.1979 | 0.9338 | 0.9340 |
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+ | 0.2092 | 0.4667 | 3500 | 0.2066 | 0.9309 | 0.9310 |
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+ | 0.2171 | 0.5333 | 4000 | 0.2058 | 0.9353 | 0.9353 |
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+ | 0.2102 | 0.6 | 4500 | 0.1999 | 0.9368 | 0.9370 |
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+ | 0.2 | 0.6667 | 5000 | 0.1967 | 0.9363 | 0.9363 |
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+ | 0.1952 | 0.7333 | 5500 | 0.2025 | 0.9358 | 0.9359 |
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+ | 0.1963 | 0.8 | 6000 | 0.2062 | 0.9374 | 0.9375 |
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+ | 0.2025 | 0.8667 | 6500 | 0.1918 | 0.9386 | 0.9388 |
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+ | 0.1839 | 0.9333 | 7000 | 0.1943 | 0.9413 | 0.9414 |
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+ | 0.2008 | 1.0 | 7500 | 0.1766 | 0.9420 | 0.9420 |
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+ | 0.1467 | 1.0667 | 8000 | 0.1948 | 0.9426 | 0.9426 |
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+ | 0.1502 | 1.1333 | 8500 | 0.1960 | 0.9413 | 0.9414 |
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+ | 0.1331 | 1.2 | 9000 | 0.1977 | 0.9443 | 0.9444 |
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+ | 0.1421 | 1.2667 | 9500 | 0.2006 | 0.9428 | 0.9428 |
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+ | 0.1375 | 1.3333 | 10000 | 0.1931 | 0.9437 | 0.9437 |
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+ | 0.1375 | 1.4 | 10500 | 0.1979 | 0.9434 | 0.9435 |
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  ### Framework versions
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