#  BERT Base Intent model
This is a fine tuned model based on Bert-Base-Uncased model. This model is used to classify intent into 3 categories- Fintech, Out of Scope and Abusive.
The base line model is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert).
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-5
- num_epochs: 3
- weight_decay:0.01

### Training results

| Training Loss | Epoch |  Validation Loss | Accuracy        | F1       |
|:-------------:|:-----:|:----------------:|:---------------:|:--------:|
| 0.114200      | 1.0   | 0.034498         | 0.991351        | 0.991346 |
| 0.024100      | 2.0   | 0.037945         | 0.992349        | 0.992355 |
| 0.009800      | 3.0   | 0.034846         | 0.993347        | 0.993345 |


### Model Description

The finetuned Hugging Face model is a variant of the BERT-base-uncased architecture, trained for intent classification 
with three labels: fintech, abusive, and out of scope. The model has undergone a fine-tuning process, where it has been
trained on a large corpus of annotated data using a supervised learning approach. The objective of the model is to 
classify incoming text data into one of the three predefined classes based on the underlying intent of the text.

The performance of the model was evaluated and it achieved high accuracy and F1 scores 
for all three classes. The model's high accuracy and robustness make it suitable for use in real-world applications, 
such as chatbots, customer service automation, and social media monitoring.
Overall, the finetuned Hugging Face model provides an effective and reliable solution for intent classification 
with three labels: fintech, abusive, and out of scope.


- **Developed by:** Jeswin MS, Venkatesh R, Kushal S Ballari
- **Model type:** Intent Classification
- **Language(s) (NLP):** English
- **Finetuned from model:** Bert-base-uncased


{"language": ["en"], "tags": ["finance"], "datasets": ["financial_phrasebank", "clinc_oos", "hate_speech_offensive"]}
transformers pytorch tensorboard bert text-classification finance en dataset:financial_phrasebank dataset:clinc_oos dataset:hate_speech_offensive arxiv:1810.04805 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #finance #en #dataset-financial_phrasebank #dataset-clinc_oos #dataset-hate_speech_offensive #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 

BERT Base Intent model
======================


This is a fine tuned model based on Bert-Base-Uncased model. This model is used to classify intent into 3 categories- Fintech, Out of Scope and Abusive.
The base line model is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-5
* num\_epochs: 3
* weight\_decay:0.01


### Training results



### Model Description


The finetuned Hugging Face model is a variant of the BERT-base-uncased architecture, trained for intent classification
with three labels: fintech, abusive, and out of scope. The model has undergone a fine-tuning process, where it has been
trained on a large corpus of annotated data using a supervised learning approach. The objective of the model is to
classify incoming text data into one of the three predefined classes based on the underlying intent of the text.


The performance of the model was evaluated and it achieved high accuracy and F1 scores
for all three classes. The model's high accuracy and robustness make it suitable for use in real-world applications,
such as chatbots, customer service automation, and social media monitoring.
Overall, the finetuned Hugging Face model provides an effective and reliable solution for intent classification
with three labels: fintech, abusive, and out of scope.


* Developed by: Jeswin MS, Venkatesh R, Kushal S Ballari
* Model type: Intent Classification
* Language(s) (NLP): English
* Finetuned from model: Bert-base-uncased



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-5
* num\_epochs: 3
* weight\_decay:0.01 ### Training results ### Model Description


The finetuned Hugging Face model is a variant of the BERT-base-uncased architecture, trained for intent classification
with three labels: fintech, abusive, and out of scope. The model has undergone a fine-tuning process, where it has been
trained on a large corpus of annotated data using a supervised learning approach. The objective of the model is to
classify incoming text data into one of the three predefined classes based on the underlying intent of the text.


The performance of the model was evaluated and it achieved high accuracy and F1 scores
for all three classes. The model's high accuracy and robustness make it suitable for use in real-world applications,
such as chatbots, customer service automation, and social media monitoring.
Overall, the finetuned Hugging Face model provides an effective and reliable solution for intent classification
with three labels: fintech, abusive, and out of scope.


* Developed by: Jeswin MS, Venkatesh R, Kushal S Ballari
* Model type: Intent Classification
* Language(s) (NLP): English
* Finetuned from model: Bert-base-uncased
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #finance #en #dataset-financial_phrasebank #dataset-clinc_oos #dataset-hate_speech_offensive #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-5
* num\_epochs: 3
* weight\_decay:0.01 ### Training results ### Model Description


The finetuned Hugging Face model is a variant of the BERT-base-uncased architecture, trained for intent classification
with three labels: fintech, abusive, and out of scope. The model has undergone a fine-tuning process, where it has been
trained on a large corpus of annotated data using a supervised learning approach. The objective of the model is to
classify incoming text data into one of the three predefined classes based on the underlying intent of the text.


The performance of the model was evaluated and it achieved high accuracy and F1 scores
for all three classes. The model's high accuracy and robustness make it suitable for use in real-world applications,
such as chatbots, customer service automation, and social media monitoring.
Overall, the finetuned Hugging Face model provides an effective and reliable solution for intent classification
with three labels: fintech, abusive, and out of scope.


* Developed by: Jeswin MS, Venkatesh R, Kushal S Ballari
* Model type: Intent Classification
* Language(s) (NLP): English
* Finetuned from model: Bert-base-uncased
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #finance #en #dataset-financial_phrasebank #dataset-clinc_oos #dataset-hate_speech_offensive #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-5
* num\_epochs: 3
* weight\_decay:0.01### Training results### Model Description


The finetuned Hugging Face model is a variant of the BERT-base-uncased architecture, trained for intent classification
with three labels: fintech, abusive, and out of scope. The model has undergone a fine-tuning process, where it has been
trained on a large corpus of annotated data using a supervised learning approach. The objective of the model is to
classify incoming text data into one of the three predefined classes based on the underlying intent of the text.


The performance of the model was evaluated and it achieved high accuracy and F1 scores
for all three classes. The model's high accuracy and robustness make it suitable for use in real-world applications,
such as chatbots, customer service automation, and social media monitoring.
Overall, the finetuned Hugging Face model provides an effective and reliable solution for intent classification
with three labels: fintech, abusive, and out of scope.


* Developed by: Jeswin MS, Venkatesh R, Kushal S Ballari
* Model type: Intent Classification
* Language(s) (NLP): English
* Finetuned from model: Bert-base-uncased