encodingai
/

mBERT-im-multilabel

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
+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.