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: []
new_classifier_model
This model is a fine-tuned version of 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