Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#2)

79a0190 about 1 year ago

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6.84 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- text-classification
	- emotion
	- pytorch
	datasets:
	- emotion
	metrics:
	- Accuracy, F1 Score
	thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
	model-index:
	- name: bhadresh-savani/bert-base-uncased-emotion
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: emotion
	type: emotion
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 0.9265
	name: Accuracy
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWQzNzA2MTFkY2RkNDMxYTFhOGUzMTdiZTgwODA3ODdmZTVhNTVjOTAwMGM5NjU1OGY0MjMzZWU0OTU2MzY1YiIsInZlcnNpb24iOjF9.f6iWK0iyU8_g32W2oMfh1ChevMsl0StI402cB6DNzJCYj9xywTnFltBY36jAJFDRK41HXdMnPMl64Bynr-Q9CA
	- type: precision
	value: 0.8859601677706858
	name: Precision Macro
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTc2ZjRmMzYzNTE0ZDQ1ZDdkYWViYWNhZDhkOTE2ZDhmMDFjZmZiZjRkZWVlMzQ3MWE4NDNlYzlmM2I4ZGM2OCIsInZlcnNpb24iOjF9.jR-gFrrBIAfiYV352RDhK3nzgqIgNCPd55OhIcCfVdVAWHQSZSJXhFyg8yChC7DwoVmUQy1Ya-d8Hflp7Wi-AQ
	- type: precision
	value: 0.9265
	name: Precision Micro
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDAyMWZjZTM5NWNjNTcyMWQzMWQyNDcyN2RlZTQyZTM4ZDQ4Y2FlNzM2OTZkMzM3YzI4YTAwNzg4MGNjZmZjZCIsInZlcnNpb24iOjF9.cmkuDmhhETKIKAL81K28oiO889sZ0hvEpZ6Ep7dW_KB9VOTFs15BzFY9vwcpdXQDugWBbB2g7r3FUgRLwIEpAg
	- type: precision
	value: 0.9265082039990273
	name: Precision Weighted
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA2NzY2NTJmZTExZWM3OGIzYzg3ZDM3Y2I5MTU3Mjg3Y2NmZGEyMjFmNjExZWM3ZDFjNzdhOTZkNTYwYWQxYyIsInZlcnNpb24iOjF9.DJgeA6ZovHoxgCqhzilIzafet8uN3-Xbx1ZYcEEc4jXzFbRtErE__QHGaaSaUQEzPp4BAztp1ageOaBoEmXSDg
	- type: recall
	value: 0.879224648382427
	name: Recall Macro
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGU3MmQ1Yjg5OGJlYTE1NWJmNGVjY2ExMDZiZjVjYmVkOGYxYWFkOTVlMDVjOWVhZGFjOGFkYzcwMGIyMTAyZCIsInZlcnNpb24iOjF9.jwgaNEBSQENlx3vojBi1WKJOQ7pSuP4Iyw4kKPsq9IUaW-Ah8KdgPV9Nm2DY1cwEtMayvVeIVmQ3Wo8PORDRAg
	- type: recall
	value: 0.9265
	name: Recall Micro
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDE3OWQ0ZGZjNzAxY2I0NGMxNDU0OWE1OGM2N2Q3OTUwYWI0NmZjMDQ3MDc0NDA4YTc2NDViM2Y0ZTMyMjYyZCIsInZlcnNpb24iOjF9.Ihc61PSO3K63t5hUSAve4Gt1tC8R_ZruZo492dTD9CsKOF10LkvrCskJJaOATjFJgqb3FFiJ8-nDL9Pa3HF-Dg
	- type: recall
	value: 0.9265
	name: Recall Weighted
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzJkYTg5YjA0YTBlNDY3ZjFjZWIzOWVhYjI4Y2YxM2FhMmUwMDZlZTE0NTIzNjMxMjE3NzgwNGFjYTkzOWM1YyIsInZlcnNpb24iOjF9.LlBX4xTjKuTX0NPK0jYzYDXRVnUEoUKVwIHfw5xUzaFgtF4wuqaYV7F0VKoOd3JZxzxNgf7JzeLof0qTquE9Cw
	- type: f1
	value: 0.8821398657055098
	name: F1 Macro
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTE4OThiMmE0NDEzZjBkY2RmZWNjMGI3YWNmNTFjNTY5NjIwNjFkZjk1ZjIxMjI4M2ZiZGJhYzJmNzVhZTU1NSIsInZlcnNpb24iOjF9.gzYyUbO4ycvP1RXnrKKZH3E8ym0DjwwUFf4Vk9j0wrg2sWIchjmuloZz0SLryGqwHiAV8iKcSBWWy61Q480XAw
	- type: f1
	value: 0.9265
	name: F1 Micro
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGM2Y2E0NjMyNmJhMTE4NjYyMjI2MTJlZjUzNmRmY2U3Yjk3ZGUyYzU2OWYzMWM2ZjY4ZTg0OTliOTY3YmI2MSIsInZlcnNpb24iOjF9.hEz_yExs6LV0RBpFBoUbnAQZHitxN57HodCJpDx0yyW6dQwWaza0JxdO-kBf8JVBK8JyISkNgOYskBY5LD4ZDQ
	- type: f1
	value: 0.9262425173620311
	name: F1 Weighted
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmMyY2NhNTRhOGMwM2M5OTQxNDQ0NjRkZDdiMDExMWFkMmI4MmYwZGQ1OGRiYmRjMmE2YTc0MGZmMWMwN2Q4MSIsInZlcnNpb24iOjF9.ljbb2L4R08NCGjcfuX1878HRilJ_p9qcDJpWhsu-5EqWCco80e9krb7VvIJV0zBfmi7Z3C2qGGRsfsAIhtQ5Dw
	- type: loss
	value: 0.17315374314785004
	name: loss
	verified: true
	verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmQwN2I2Nzg4OWU1ODE5NTBhMTZiMjljMjJhN2JiYmY0MTkzMTA1NmVhMGU0Y2Y0NjgyOTU3ZjgyYTc3ODE5NCIsInZlcnNpb24iOjF9.EEp3Gxm58ab-9335UGQEk-3dFQcMRgJgViI7fpz7mfY2r5Pg-AOel5w4SMzmBM-hiUFwStgxe5he_kG2yPGFCw
	---
	# bert-base-uncased-emotion

	## Model description:

	[Bert](https://arxiv.org/abs/1810.04805) 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 emotion dataset using HuggingFace Trainer with below training parameters
	```
	learning rate 2e-5,
	batch size 64,
	num_train_epochs=8,
	```

	## Model Performance Comparision on Emotion Dataset from Twitter:

	\| Model \| Accuracy \| F1 Score \| Test Sample per Second \|
	\| --- \| --- \| --- \| --- \|
	\| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) \| 93.8 \| 93.79 \| 398.69 \|
	\| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) \| 94.05 \| 94.06 \| 190.152 \|
	\| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) \| 93.95 \| 93.97\| 195.639 \|
	\| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) \| 93.6 \| 93.65 \| 182.794 \|

	## How to Use the model:
	```python
	from transformers import pipeline
	classifier = pipeline("text-classification",model='bhadresh-savani/bert-base-uncased-emotion', return_all_scores=True)
	prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
	print(prediction)

	"""
	output:
	[[
	{'label': 'sadness', 'score': 0.0005138228880241513},
	{'label': 'joy', 'score': 0.9972520470619202},
	{'label': 'love', 'score': 0.0007443308713845909},
	{'label': 'anger', 'score': 0.0007404946954920888},
	{'label': 'fear', 'score': 0.00032938539516180754},
	{'label': 'surprise', 'score': 0.0004197491507511586}
	]]
	"""
	```

	## Dataset:
	[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).

	## Training procedure
	[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
	follow the above notebook by changing the model name from distilbert to bert

	## Eval results
	```json
	{
	'test_accuracy': 0.9405,
	'test_f1': 0.9405920712282673,
	'test_loss': 0.15769127011299133,
	'test_runtime': 10.5179,
	'test_samples_per_second': 190.152,
	'test_steps_per_second': 3.042
	}
	```

	## Reference:
	* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)