metadata

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 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

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