--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: new_classifier_model results: [] language: en widget: - text: "In the case of (ioii) and (1 lii), the passive transformation will apply to the embedded sentence, and in all four cases other operations will give the final surface forms of (8) and (g)." - text: "(10) (i) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — a specialist — a specialist will examine John) (ii) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — John — a specialist will examine John)" - text: "184 SOME RESIDUAL PROBLEMS" - text: "Peshkovskii, A. M. (1956). Russkii Sintaksis v Nauchnom Osveshchenii. Moscow." - text: "S -» NP^Aux^VP" - text: "(sincerity, [+N, —Count, +Abstract]) (boy, [+N, —Count, +Common, +Animate, +Human]) (may, [+M])" --- # Classifier for Academic Text Contents This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on a collection of Linguistics publications. It achieves the following results on the evaluation set: - Loss: 0.4181 - Accuracy: 0.9193 ## Model description The model is fine-tuned with academic publications in Linguistics, to classify texts in publications into 4 classes as a filter to other tasks. Sentence-based data obtained from OCR-processed PDF files was annotated manually with the following classes: - 0: out of scope - materials that are of low significance, eg. page number and page header, noise from OCR/pdf-to-text convertion - 1: main text - texts that are the main texts of the publication, to be used for down-stream tasks - 2: examples - texts that are captions of the figures, or quotes or excerpts - 3: references - references of the publication, excluding in-text citations ## Intended uses & limitations Intended uses: - to extract main text in academic texts for down-stream tasks Limitations: - training and evaluation data is limited to English, and academic texts in Linguistics (though still to a higher extent usable for German texts) ## How to run ```python from transformers import pipeline # return output for the best label # eg. [{'label': 'EXAMPLE', 'score': 0.9601941108703613}] classifier = pipeline("text-classification", model="howanching-clara/classifier_for_academic_texts", tokenizer="howanching-clara/classifier_for_academic_texts") # return output for all labels # eg. [[{'label': 'OUT OF SCOPE', 'score': 0.007808608002960682}, {'label': 'MAIN TEXT', 'score': 0.028077520430088043}, {'label': 'EXAMPLE', 'score': 0.9601941108703613}, {'label': 'REFERENCE', 'score': 0.003919811453670263}]] # classifier = pipeline("text-classification", model="howanching-clara/classifier_for_academic_texts", tokenizer="howanching-clara/classifier_for_academic_texts", return_all_scores=True) # Perform inference on your input text your_text = "your text here." result = classifier(your_text) print(result) ``` ## Try it yourself with the following examples (not in training/ evaluation data) Excerpts from Chomsky, N. (2014). Aspects of the Theory of Syntax (No. 11). MIT press. retrieved from https://apps.dtic.mil/sti/pdfs/AD0616323.pdf - In the case of (ioii) and (1 lii), the passive transformation will apply to the embedded sentence, and in all four cases other operations will give the final surface forms of (8) and (g). - (10) (i) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — a specialist — a specialist will examine John) (ii) Noun Phrase — Verb — Noun Phrase — Sentence (/ — persuaded — John — a specialist will examine John) - (13) S Det Predicate-Phrase [+Definite] nom VP their F1...Fm Det N destroy [+Definite] G, ... G, the property - 184 SOME RESIDUAL PROBLEMS - Peshkovskii, A. M. (1956). Russkii Sintaksis v Nauchnom Osveshchenii. Moscow. - S -» NP^Aux^VP - (sincerity, [+N, —Count, +Abstract]) (boy, [+N, —Count, +Common, +Animate, +Human]) (may, [+M]) ## Problematic cases Definitions or findings written in point form are challenging for the model. For example: - (2) (i) the string (1) is a Sentence (S); frighten the boy is a Verb Phrase (VP) consisting of the Verb (V) frighten and the Noun Phrase (NP) the boy; sincerity is also an NP; the NP the boy consists of the Determiner (Det) the, followed by a Noun (N); the NP sincerity consists of just an N; the is, furthermore, an Article (Art); may is a Verbal Auxiliary (Aux) and, furthermore, a Modal (M). - (v) specification of a function m such that m(i) is an integer associated with the grammar G4 as its value (with, let us say, lower value indicated by higher number) ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5772 | 1.0 | 762 | 0.3256 | 0.9062 | | 0.2692 | 2.0 | 1524 | 0.3038 | 0.9163 | | 0.217 | 3.0 | 2286 | 0.3109 | 0.9180 | | 0.1773 | 4.0 | 3048 | 0.3160 | 0.9209 | | 0.1619 | 5.0 | 3810 | 0.3440 | 0.9206 | | 0.1329 | 6.0 | 4572 | 0.3675 | 0.9160 | | 0.1165 | 7.0 | 5334 | 0.3770 | 0.9209 | | 0.0943 | 8.0 | 6096 | 0.4012 | 0.9203 | | 0.085 | 9.0 | 6858 | 0.4166 | 0.9196 | | 0.0811 | 10.0 | 7620 | 0.4181 | 0.9193 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cpu - Datasets 2.14.7 - Tokenizers 0.14.1