---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# new_classifier_model

This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset.
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.

The 4 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

## 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.




## 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