aaronayitey/Sentiment-classfication-distilBERT-model

README.md

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+---
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Sentiment-classfication-distilBERT-model
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Sentiment-classfication-distilBERT-model
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3217
+- Accuracy: 0.9301
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.11          | 0.14  | 100  | 1.0458          | 0.4603   |
+| 0.9647        | 0.27  | 200  | 0.9241          | 0.5743   |
+| 0.8498        | 0.41  | 300  | 0.7957          | 0.6365   |
+| 0.7436        | 0.54  | 400  | 0.7044          | 0.7043   |
+| 0.683         | 0.68  | 500  | 0.7109          | 0.7040   |
+| 0.6407        | 0.81  | 600  | 0.5602          | 0.7872   |
+| 0.5388        | 0.95  | 700  | 0.5073          | 0.8031   |
+| 0.449         | 1.09  | 800  | 0.4736          | 0.8316   |
+| 0.4136        | 1.22  | 900  | 0.5387          | 0.8147   |
+| 0.3329        | 1.36  | 1000 | 0.4277          | 0.8615   |
+| 0.3405        | 1.49  | 1100 | 0.3667          | 0.8730   |
+| 0.2806        | 1.63  | 1200 | 0.3420          | 0.8832   |
+| 0.2648        | 1.77  | 1300 | 0.3437          | 0.8975   |
+| 0.2912        | 1.9   | 1400 | 0.3503          | 0.8914   |
+| 0.2109        | 2.04  | 1500 | 0.3268          | 0.9182   |
+| 0.1267        | 2.17  | 1600 | 0.3676          | 0.9182   |
+| 0.0931        | 2.31  | 1700 | 0.3635          | 0.9250   |
+| 0.1447        | 2.44  | 1800 | 0.3144          | 0.9233   |
+| 0.0979        | 2.58  | 1900 | 0.3197          | 0.9301   |
+| 0.1156        | 2.72  | 2000 | 0.3217          | 0.9301   |
+| 0.0922        | 2.85  | 2100 | 0.3323          | 0.9294   |
+| 0.1094        | 2.99  | 2200 | 0.2976          | 0.9304   |
+| 0.0667        | 3.12  | 2300 | 0.3554          | 0.9318   |
+| 0.0479        | 3.26  | 2400 | 0.3648          | 0.9318   |
+| 0.0427        | 3.39  | 2500 | 0.3615          | 0.9331   |
+| 0.0499        | 3.53  | 2600 | 0.3251          | 0.9389   |
+| 0.0381        | 3.67  | 2700 | 0.3391          | 0.9362   |
+| 0.0498        | 3.8   | 2800 | 0.3350          | 0.9365   |
+| 0.0565        | 3.94  | 2900 | 0.3331          | 0.9375   |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0+cu118
+- Datasets 2.14.6
+- Tokenizers 0.14.1

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