---
# The Office Speaker Classification Model

This model is designed to classify speakers (characters) of the TV show The Office based on a given line. It is built on top of the popular DistilBertForSequenceClassification model.

## How to Use

```python
from transformers import DistilBertForSequenceClassification, DistilBertTokenizer

model_name = 'mo374z/theoffice_speaker_classification'
model = DistilBertForSequenceClassification.from_pretrained(model_name)
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
```

Once the model and tokenizer are loaded, you can classify the speaker of a given line as follows:

```python
test_sample_str = "Hi i'm Michael Scott"
test_sample = tokenizer.encode(test_sample_str, truncation=True, padding=True, return_tensors='pt')
test_sample = test_sample.to(device)
output = model(test_sample)
```

To get an explanation for the model prediction, you can use the SHAP explainer. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction.

## SHAP
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It can be used to explain the contribution of each input feature to the model's prediction. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction.

## License
This model is licensed under the MIT License.
{"language": "en", "license": "mit", "widget": [{"text": "Hi i am Michael Scott.", "example_title": "Example 1"}, {"text": "And this is how to stab someone in the back.", "example_title": "Example 2"}, {"text": "Good to see you!", "example_title": "Example 3"}], "pipeline_tag": "text-classification"}
transformers pytorch distilbert text-classification en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 

---
# The Office Speaker Classification Model

This model is designed to classify speakers (characters) of the TV show The Office based on a given line. It is built on top of the popular DistilBertForSequenceClassification model.

## How to Use



Once the model and tokenizer are loaded, you can classify the speaker of a given line as follows:



To get an explanation for the model prediction, you can use the SHAP explainer. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction.

## SHAP
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It can be used to explain the contribution of each input feature to the model's prediction. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction.

## License
This model is licensed under the MIT License.
# The Office Speaker Classification Model

This model is designed to classify speakers (characters) of the TV show The Office based on a given line. It is built on top of the popular DistilBertForSequenceClassification model. ## How to Use



Once the model and tokenizer are loaded, you can classify the speaker of a given line as follows:



To get an explanation for the model prediction, you can use the SHAP explainer. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction. ## SHAP
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It can be used to explain the contribution of each input feature to the model's prediction. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction. ## License
This model is licensed under the MIT License.
TAGS
#transformers #pytorch #distilbert #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # The Office Speaker Classification Model

This model is designed to classify speakers (characters) of the TV show The Office based on a given line. It is built on top of the popular DistilBertForSequenceClassification model. ## How to Use



Once the model and tokenizer are loaded, you can classify the speaker of a given line as follows:



To get an explanation for the model prediction, you can use the SHAP explainer. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction. ## SHAP
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It can be used to explain the contribution of each input feature to the model's prediction. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction. ## License
This model is licensed under the MIT License.
TAGS
#transformers #pytorch #distilbert #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# The Office Speaker Classification Model

This model is designed to classify speakers (characters) of the TV show The Office based on a given line. It is built on top of the popular DistilBertForSequenceClassification model.## How to Use



Once the model and tokenizer are loaded, you can classify the speaker of a given line as follows:



To get an explanation for the model prediction, you can use the SHAP explainer. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction.## SHAP
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It can be used to explain the contribution of each input feature to the model's prediction. By using SHAP, you can gain insights into the speaking style of the characters, and understand which words and phrases are most important for the model's prediction.## License
This model is licensed under the MIT License.