README.md

---
license: cc-by-nc-nd-4.0
language:
- en
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 the classifier 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 > 0.86 Pearson correlation on the 
Winter (1994) training data (see the [OSF repo](https://osf.io/aurwb/) for the benchmark dataset). 
Development of this classifier is ongoing, as it achieves only > ~0.7 Pearson correlation on larger 
unseen datasets, though all correlations are highly significant.

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
-----
Pearson correlations:
| Pow (Label_0): 0.81695 |
| Ach (Label_1): 0.92673 |
| Aff (Label_2): 0.85618 |
| mean: 0.86544 |

Category agreement:
| Pow (Label_0): | 0.84034 |
| Ach (Label_1): | 0.93274 |
| Aff (Label_2): | 0.87429 |
| mean: | 0.88163 |

Intra-class Correlation Coefficient:
| Pow (Label_0): | 0.89889 |
| Ach (Label_1): | 0.96200 |
| Aff (Label_2): | 0.92231 |
| mean: | 0.92736 |
```

## 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)

# The labels are arranged according to likelihood of classification
repdict = {"LABEL_0": "Pow", "LABEL_1": "Ach", "LABEL_2": "Aff"}
# so we replace them in the output
scores = {repdict[x['label']]: x['score'] for x in response.json()}
print(scores)

# output: {'Pow': 0.8279141187667847, 'Aff': 0.7250356674194336, 'Ach': 0.0020263446494936943}
```

## 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.