| --- |
| 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 a version of a classifier for implicit motives based on ModernBert. The classifier identifies the |
| presence of implicit motive imagery in sentences, namely the three felt needs for Power, Achievement, |
| and Affiliation. |
|
|
| This model is being made available to other researchers via download. 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 |
|
|
| This model can be directly downloaded and used with the following code. |
|
|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
| |
| mbert = "encodingai/mBERT-im-multilabel" |
| |
| tokenizer = AutoTokenizer.from_pretrained(mbert, use_fast=True) |
| model = AutoModelForSequenceClassification.from_pretrained(mbert, |
| problem_type="multi_label_classification", |
| ) |
| |
| # load model using the pipeline, returning the top 3 classifications |
| classifier = pipeline("text-classification", model=model, device=0, tokenizer=tokenizer, top_k=3) |
| |
| sample = ["""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."""] |
| |
| # predict on a sentence |
| pred = classifier(sample) |
| print(pred) |
| # 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 |
| for y in pred: |
| scores = {repdict[x['label']]: x['score'] for x in y} |
| print(scores) |
| ``` |
|
|
| ## 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. |
