---
language:
- en
license: cc-by-nc-nd-4.0
library_name: transformers
pipeline_tag: text-classification
widget:
- text: Mr. Jones, an architect is going to surprise his family by building them a
    new house.
  example_title: Pow
- text: They want the research to go well and be productive.
  example_title: Ach
- text: The man is trying to see a friend on board, but the officer will not let him
    go as the whistle for all ashore who are not going has already blown.
  example_title: Aff
- text: The recollection of skating on the Charles, and the time she had pushed me
    through the ice, brought a laugh to the conversation; but it quickly faded in
    the murky waters of the river that could no longer freeze over.
  example_title: Pow + Aff
- text: They are also well-known research scientists and are quite talented in this
    field.
  example_title: Pow + Ach
- text: After a nice evening with his family, he will be back at work tomorrow, doing
    the best job he can on his drafting.
  example_title: Ach + Aff
- text: She is surprised that she is able to make these calls and pleasantly surprised
    that her friends respond to her request.
  example_title: Pow + Aff
---
This is an updated version of [https://huggingface.co/encodingai/electra-base-discriminator-im-multilabel](https://huggingface.co/encodingai/electra-base-discriminator-im-multilabel),
reported in [Pang & Ring (2020)](https://rdcu.be/b38pm) 
and found at [implicitmotives.com](https://implicitmotives.com). The classifier identifies the 
presence of implicit motive imagery in sentences, namely the three felt needs for Power, Achievement, 
and Affiliation.

The current classifier is finetuned from ELECTRA-base and achieves > 0.90 ICC on the 
Winter (1994) training data (see the [OSF repo](https://osf.io/aurwb/) for the benchmark dataset). 
Development of this classifier is ongoing, and the current version has been trained on a larger and 
more diverse dataset, which means it generalizes better to unseen data.

This model is being made available to other researchers for inference via a Huggingface api. The 
current license allows for free use without modification for non-commercial purposes. If you would 
like to use this model commercially, get in touch with us for access to our most recent model.

```
Predictions on Winter manual dataset
-----
Intra-class Correlation Coefficient:
| Pow (Label_0): | 0.90469 |
| Ach (Label_1): | 0.93134 |
| Aff (Label_2): | 0.88893 |
| mean: | 0.90815 |

Pearson correlations:
| Pow (Label_0): 0.81604 |
| Ach (Label_1): 0.85726 |
| Aff (Label_2): 0.77257 |
| mean: 0.81455 |

```

## Inference guide

The inference api requires a Huggingface token. The sample code below illustrates how it can be used to classify individual sentences.

```python
import json
import requests
api_key = "<HF Token>"
headers = {"Authorization": f"Bearer {api_key}"}
api_url = "https://api.url.here"

# This is a sentence from the Winter manual that is dual-scored for both Pow and Aff
prompt = """The recollection of skating on the Charles, and the time she had
            pushed me through the ice, brought a laugh to the conversation; but
            it quickly faded in the murky waters of the river that could no
            longer freeze over."""

# Since this is a multilabel classifier, we want to return scores for the top 3 labels
data = {"inputs": prompt, "parameters": {"top_k": 3}}

response = requests.request("POST", api_url, headers=headers, json=data)

# Print the labels and scores (arranged in order of likelihood)
scores = {x['label']: x['score'] for x in response.json()}
print(scores)

# {'Aff': 0.999998927116394, 'Pow': 0.999890923500061, 'Ach': 5.351924119167961e-05}
```

## References

McClelland, D. C. (1965). Toward a theory of motive acquisition. American Psychologist, 20,321-333.

Pang, J. S., & Ring, H. (2020). Automated Coding of Implicit Motives: A Machine-Learning Approach. 
Motivation and Emotion, 44(4), 549-566. DOI: 10.1007/s11031-020-09832-8.

Winter, D.G. (1994). Manual for scoring motive imagery in running text. Unpublished Instrument. Ann Arbor: University of Michigan.