Update README.md

README.md

@@ -4,6 +4,7 @@ license: mit
 base_model: FacebookAI/xlm-roberta-base
 tags:
 - generated_from_trainer
+language: en
 metrics:
 - accuracy
 - precision
@@ -12,12 +13,20 @@ metrics:
 model-index:
 - name: academic_main_text_classifier_en
   results: []
+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])"
+
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# academic_main_text_classifier_en
+# Academic Main Text_Classifier (en)
 
 This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
@@ -29,11 +38,100 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+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
 
-More information needed
+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
+
+# define model name
+model_name = "ubffm/academic_text_filter"
+
+# run model with hf pipeline
+## return output for the best label
+## eg. [{'label': 'EXAMPLE', 'score': 0.9601941108703613}]
+classifier = pipeline("text-classification", model=model_name, tokenizer=model_name)
+
+## 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=model_name, tokenizer=model_name, 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