| | --- |
| | 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. |
