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.
{"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"}]}
transformers pytorch electra text-classification en license:cc-by-nc-nd-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #en #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #region-us 

This is an updated version of the classifier reported in Pang & Ring (2020) 
and found at URL. 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 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.



## Inference guide

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



## 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.
## Inference guide

The inference api requires a Huggingface token. The sample code below illustrates how it can be used to classify individual sentences. ## 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.
TAGS
#transformers #pytorch #electra #text-classification #en #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Inference guide

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