## 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}
```
 
{"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "sentiment-analysis", "poem-sentiment-detection", "poem-sentiment"], "datasets": ["poem_sentment"], "metrics": ["Accuracy, F1 score"], "thumbnail": "https://cdn.theatlantic.com/thumbor/lx3Vy9ojT2A0mHVooAUARLRpUEc=/0x215:3500x2184/976x549/media/img/mt/2018/03/RTR4F51Y/original.jpg", "widget": [{"text": "No man is an island, Entire of itself, Every man is a piece of the continent, A part of the main.", "example_title": "No man is an island"}, {"text": "Ten years, dead and living dim and draw apart. I don\u2019t try to remember, But forgetting is hard.", "example_title": "Dreaming of my departed wife"}, {"text": "My mind to me a kingdom is; Such present joys therein I find,That it excels all other bliss.", "example_title": "My mind to me a kingdom is"}]}
transformers pytorch bert text-classification sentiment-analysis poem-sentiment-detection poem-sentiment en dataset:poem_sentment license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #sentiment-analysis #poem-sentiment-detection #poem-sentiment #en #dataset-poem_sentment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

nickwong64/bert-base-uncased-poems-sentiment
--------------------------------------------


Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective.
bert-base-uncased finetuned on the poem\_sentiment dataset using HuggingFace Trainer with below training parameters.


Model Performance
-----------------



How to Use the Model
--------------------


Dataset
-------


poem\_sentiment


Labels
------


Evaluation
----------



TAGS
#transformers #pytorch #bert #text-classification #sentiment-analysis #poem-sentiment-detection #poem-sentiment #en #dataset-poem_sentment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #text-classification #sentiment-analysis #poem-sentiment-detection #poem-sentiment #en #dataset-poem_sentment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
