# Model Card for Model ID

This model was developed by fine-tuning [BLOOMZ-3b] (https://huggingface.co/bigscience/bloomz-3b) using the [Misconceptions] (https://huggingface.co/datasets/indikamk/misconceptions) dataset.


## Model Details

### Model Description

This model can be used to identify whether a sentence written by a student about an electrical circuit contains a "Sequential" Misconception. 

A sequential misconception in terms of electric circuits is one in which it is believed that elements that are further “downstream” from a source “receive” current after elements closer to the source.



- **Developed by:** Indika Kahanda
- **License:** CC BY 4.0
- **Finetuned from model:** [BLOOMZ-3b] (https://huggingface.co/bigscience/bloomz-3b)

## Uses

The model can be used to generate feedback by inputting a sentence written by a student about a simple electrical circuit
The feedback would indicate whether the sentence contains a Squenctila misconception or not.


## Training Details

### Training Data

This model was finetuned using the [Misconceptions] (https://huggingface.co/datasets/indikamk/misconceptions) dataset.








{"language": ["en"], "license": "cc-by-4.0", "library_name": "adapter-transformers", "datasets": ["indikamk/misconceptions"], "metrics": ["accuracy"], "pipeline_tag": "text2text-generation"}
adapter-transformers pytorch bloom text2text-generation en dataset:indikamk/misconceptions license:cc-by-4.0 region:us
TAGS
#adapter-transformers #pytorch #bloom #text2text-generation #en #dataset-indikamk/misconceptions #license-cc-by-4.0 #region-us 

# Model Card for Model ID

This model was developed by fine-tuning [BLOOMZ-3b] (URL using the [Misconceptions] (URL dataset.


## Model Details

### Model Description

This model can be used to identify whether a sentence written by a student about an electrical circuit contains a "Sequential" Misconception. 

A sequential misconception in terms of electric circuits is one in which it is believed that elements that are further “downstream” from a source “receive” current after elements closer to the source.



- Developed by: Indika Kahanda
- License: CC BY 4.0
- Finetuned from model: [BLOOMZ-3b] (URL

## Uses

The model can be used to generate feedback by inputting a sentence written by a student about a simple electrical circuit
The feedback would indicate whether the sentence contains a Squenctila misconception or not.


## Training Details

### Training Data

This model was finetuned using the [Misconceptions] (URL dataset.








# Model Card for Model ID

This model was developed by fine-tuning [BLOOMZ-3b] (URL using the [Misconceptions] (URL dataset. ## Model Details ### Model Description

This model can be used to identify whether a sentence written by a student about an electrical circuit contains a "Sequential" Misconception. 

A sequential misconception in terms of electric circuits is one in which it is believed that elements that are further “downstream” from a source “receive” current after elements closer to the source.



- Developed by: Indika Kahanda
- License: CC BY 4.0
- Finetuned from model: [BLOOMZ-3b] (URL ## Uses

The model can be used to generate feedback by inputting a sentence written by a student about a simple electrical circuit
The feedback would indicate whether the sentence contains a Squenctila misconception or not. ## Training Details ### Training Data

This model was finetuned using the [Misconceptions] (URL dataset.
TAGS
#adapter-transformers #pytorch #bloom #text2text-generation #en #dataset-indikamk/misconceptions #license-cc-by-4.0 #region-us 
 # Model Card for Model ID

This model was developed by fine-tuning [BLOOMZ-3b] (URL using the [Misconceptions] (URL dataset. ## Model Details ### Model Description

This model can be used to identify whether a sentence written by a student about an electrical circuit contains a "Sequential" Misconception. 

A sequential misconception in terms of electric circuits is one in which it is believed that elements that are further “downstream” from a source “receive” current after elements closer to the source.



- Developed by: Indika Kahanda
- License: CC BY 4.0
- Finetuned from model: [BLOOMZ-3b] (URL ## Uses

The model can be used to generate feedback by inputting a sentence written by a student about a simple electrical circuit
The feedback would indicate whether the sentence contains a Squenctila misconception or not. ## Training Details ### Training Data

This model was finetuned using the [Misconceptions] (URL dataset.