text-classification en dataset:Dh1raj/resume-dataset dataset:Dh1raj/resume-labelled-encoded license:apache-2.0 region:us
---
datasets:
- Dh1raj/resume-dataset
- Dh1raj/resume-labelled-encoded
language:
- en
license: apache-2.0
pipeline_tag: text-classification
---



This modelcard is a fine tuned version of Bert-base-uncased for text classification given candidates profile description on the resume. 
## Model Details



- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- Finetuned from model [bert-base-uncased]:


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Risks inherited from base model apply

## How to Get Started with the Model

## Training Details

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

## Evaluation

Step	Training Loss	Validation Loss	Accuracy	F1	Precision Recall
50	3.191700	3.144314	0.045576	0.005990	0.043515	0.042929
100	2.940400	2.529969	0.313673	0.198400	0.326865	0.263533
150	2.289300	1.829516	0.638070	0.522103	0.562795	0.570456
200	1.582400	1.251463	0.784182	0.678805	0.699407	0.707561
250	1.120000	1.036136	0.773458	0.668376	0.700246	0.693412
300	0.886400	0.898808	0.812332	0.733046	0.757457	0.741408
350	0.799100	0.825964	0.824397	0.745086	0.766041	0.752816
400	0.848900	0.734574	0.836461	0.773499	0.812788	0.768352
450	0.699000	0.745959	0.833780	0.759005	0.780415	0.763101
500	0.510300	0.710035	0.844504	0.771996	0.794520	0.775719
550	0.514200	0.679396	0.840483	0.768999	0.785576	0.773328
600	0.516700	0.684477	0.843164	0.770532	0.785427	0.774498
650	0.483200	0.686520	0.840483	0.768139	0.780840	0.772857


#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

	eval_loss	eval_Accuracy	eval_F1	eval_Precision	eval_Recall
train	0.479060	0.897583	0.839754	0.911787	0.840669
val	0.710035	0.844504	0.771996	0.794520	0.775719

#### Hardware

colab T4

## Model Card Contact

dhirajkr@live.com