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feedback-classifier – AI Model by iam-tsr | AlphaNeural AI
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iam-tsr
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feedback-classifier
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peft
joblib
onnx
safetensors
distilbert
text-classification
adapter
lora
transformers
en
iam-tsr/employ_fdbk
distilbert/distilbert-base-uncased
apache-2.0
text-embeddings-inference
endpoints_compatible
us
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Model card
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results
This model is a fine-tuned version of
distilbert/distilbert-base-uncased
on
employ feedback dataset
. It achieves the following results on the evaluation set:
Loss: 0.1520
Accuracy: 0.9423
Precision: 0.9181
Recall: 0.9284
F1: 0.9228
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.001
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
Precision
Recall
F1
0.1735
1.0
172
0.1631
0.9462
0.9249
0.9297
0.9272
0.1824
2.0
344
0.1619
0.9385
0.9108
0.9308
0.9191
0.1555
3.0
516
0.1520
0.9423
0.9181
0.9284
0.9228
Framework versions
PEFT 0.18.0
Transformers 4.57.3
Pytorch 2.9.1+cu128
Datasets 4.3.0
Tokenizers 0.22.2
