Create README.md

README.md

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+---
+language: 
+  - en
+thumbnail:  https://cdn.theatlantic.com/thumbor/lx3Vy9ojT2A0mHVooAUARLRpUEc=/0x215:3500x2184/976x549/media/img/mt/2018/03/RTR4F51Y/original.jpg 
+tags:
+- text-classification
+- sentiment-analysis
+- poem-sentiment-detection
+- poem-sentiment
+license: apache-2.0
+datasets:
+- poem_sentment
+metrics:
+- Accuracy, F1 score
+
+---
+## nickwong64/bert-base-uncased-poems-sentiment
+Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective.
+[bert-base-uncased](https://huggingface.co/bert-base-uncased) finetuned on the [poem_sentiment](https://huggingface.co/datasets/poem_sentiment) dataset using HuggingFace Trainer with below training parameters.
+```
+ learning rate 2e-5, 
+ batch size 8,
+ num_train_epochs=8,
+```
+
+ ## Model Performance
+| Epoch | Training Loss | Validation Loss | Accuracy | F1 |
+| --- | --- | --- | --- | --- |
+| 8 | 0.468200	| 0.458632 | 0.904762 | 0.899756 |
+
+
+
+ ## How to Use the Model
+```python 
+from transformers import pipeline
+nlp = pipeline(task='text-classification', 
+               model='nickwong64/bert-base-uncased-poems-sentiment')
+p1 = "No man is an island, Entire of itself, Every man is a piece of the continent, A part of the main."
+p2 = "Ten years, dead and living dim and draw apart. I don’t try to remember, But forgetting is hard."
+p3 = "My mind to me a kingdom is; Such present joys therein I find,That it excels all other bliss"
+print(nlp(p1))
+print(nlp(p2))
+print(nlp(p3))
+"""
+output:
+[{'label': 'no_impact', 'score': 0.9982421398162842}]
+[{'label': 'negative', 'score': 0.9856176972389221}]
+[{'label': 'positive', 'score': 0.9931322932243347}]
+"""
+```
+
+## Dataset
+  [poem_sentiment](https://huggingface.co/datasets/poem_sentiment)
+
+## Labels
+```
+{0: 'negative', 1: 'positive', 2: 'no_impact', 3: 'mixed'}
+```
+
+## Evaluation
+```
+{'test_loss': 0.4359096586704254,
+ 'test_accuracy': 0.9142857142857143,
+ 'test_f1': 0.9120554830816401,
+ 'test_runtime': 0.5689,
+ 'test_samples_per_second': 184.582,
+ 'test_steps_per_second': 24.611}
+```
+