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--- |
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license: mit |
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base_model: openai-community/gpt2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: gpt2-lang-ident |
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results: [] |
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pipeline_tag: text-classification |
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language: |
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- af |
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- am |
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- ar |
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- as |
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- az |
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- ba |
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- be |
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- bg |
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- bn |
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- ca |
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- ceb |
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- ckb |
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- cs |
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- cy |
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- da |
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- de |
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- dv |
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- el |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- fa |
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- fi |
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- fr |
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- fy |
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- ga |
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- gd |
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- gl |
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- gu |
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- he |
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- hi |
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- hr |
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- hu |
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- hy |
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- id |
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- is |
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- it |
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- ja |
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- ka |
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- kk |
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- kn |
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- ku |
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- ky |
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- la |
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- lb |
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- lt |
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- lv |
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- mg |
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- mk |
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- ml |
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- mn |
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- mr |
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- mt |
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- my |
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- nds |
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- ne |
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- nl |
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- nn |
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- no |
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- or |
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- pa |
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- pl |
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- ps |
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- pt |
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- ro |
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- ru |
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- sah |
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- sd |
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- si |
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- sk |
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- sl |
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- sq |
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- sr |
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- sv |
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- sw |
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- ta |
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- te |
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- tg |
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- th |
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- tk |
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- tl |
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- tr |
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- tt |
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- ug |
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- uk |
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- ur |
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- vi |
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- yi |
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--- |
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# gpt2-lang-ident |
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This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on sampled sentences from `stanford-oval/ccnews` and `qanastek/EMEA-V3` datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1210 |
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- Accuracy: 0.9721 |
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## Model description |
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This model is trained to predict the language of the input text. |
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## Intended uses & limitations |
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This model can predict the following 90 languages: |
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``` |
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[ |
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"af", "am", "ar", "as", "az", "ba", "be", "bg", "bn", "ca", |
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"ceb", "ckb", "cs", "cy", "da", "de", "dv", "el", "en", "eo", |
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"es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", |
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"gu", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", |
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"ka", "kk", "kn", "ku", "ky", "la", "lb", "lt", "lv", "mg", |
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"mk", "ml", "mn", "mr", "mt", "my", "nds", "ne", "nl", "nn", |
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"no", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sah", "sd", |
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"si", "sk", "sl", "sq", "sr", "sv", "sw", "ta", "te", "tg", |
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"th", "tk", "tl", "tr", "tt", "ug", "uk", "ur", "vi", "yi" |
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] |
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``` |
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How to use: |
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```python |
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from transformers import (AutoModelForSequenceClassification, AutoTokenizer, |
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pipeline) |
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checkpoint = f"nie3e/gpt2-lang-ident" |
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model = AutoModelForSequenceClassification.from_pretrained(checkpoint) |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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pipe = pipeline( |
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task="text-classification", |
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model=model, |
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tokenizer=tokenizer, |
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top_k=5 |
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) |
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result = pipe("To jest model służący do identyfikacji języka!") |
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print(result) |
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``` |
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``` |
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[[{'label': 'pl', 'score': 0.9999653100967407}, {'label': 'sr', 'score': 1.5228776646836195e-05}, {'label': 'hr', 'score': 1.057955432770541e-05}, {'label': 'bn', 'score': 1.590750912328076e-06}, {'label': 'cs', 'score': 1.3942196801508544e-06}]] |
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``` |
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## Training and evaluation data |
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<details><summary>Training data ([lang]: count)</summary> |
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[bn]: 1947 |
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[ar]: 1947 |
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[vi]: 1947 |
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[uk]: 1947 |
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[kn]: 1947 |
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[mr]: 1947 |
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[id]: 1947 |
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[te]: 1947 |
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[no]: 1947 |
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[ru]: 1947 |
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[he]: 1947 |
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[az]: 1947 |
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[ca]: 1946 |
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[fa]: 1946 |
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[hi]: 1946 |
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[th]: 1946 |
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[tr]: 1946 |
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[mk]: 1946 |
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[ta]: 1945 |
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[sq]: 1945 |
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[ur]: 1942 |
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[gu]: 1939 |
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[ml]: 1936 |
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[is]: 1738 |
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[de]: 1543 |
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[da]: 1521 |
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[fi]: 1461 |
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[el]: 1431 |
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[nl]: 1424 |
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[fr]: 1408 |
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[cs]: 1401 |
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[es]: 1397 |
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[en]: 1394 |
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[lt]: 1392 |
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[hu]: 1379 |
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[pt]: 1375 |
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[lv]: 1373 |
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[it]: 1360 |
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[pl]: 1355 |
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[sk]: 1355 |
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[et]: 1348 |
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[sl]: 1328 |
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[sv]: 1300 |
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[bg]: 1278 |
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[mt]: 1234 |
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[ro]: 1218 |
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[kk]: 1179 |
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[hy]: 1176 |
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[or]: 1112 |
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[pa]: 780 |
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[sr]: 744 |
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[as]: 735 |
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[hr]: 722 |
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[ne]: 626 |
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[gl]: 566 |
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[ckb]: 563 |
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[ka]: 560 |
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[ug]: 485 |
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[ky]: 453 |
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[eu]: 351 |
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[ps]: 311 |
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[tl]: 307 |
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[fy]: 290 |
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[mn]: 289 |
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[si]: 244 |
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[cy]: 214 |
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[nn]: 212 |
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[ku]: 195 |
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[tg]: 176 |
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[am]: 141 |
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[tt]: 121 |
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[ja]: 104 |
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[lb]: 93 |
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[tk]: 72 |
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[be]: 64 |
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[sw]: 45 |
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[af]: 44 |
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[my]: 40 |
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[ceb]: 35 |
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[la]: 33 |
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[dv]: 20 |
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[ba]: 19 |
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[ga]: 19 |
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[eo]: 19 |
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[gd]: 16 |
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[mg]: 15 |
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[yi]: 14 |
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[sah]: 14 |
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[sd]: 11 |
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[nds]: 11 |
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</details> |
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<details><summary>Eval data ([lang]: count)</summary> |
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[te]: 195 |
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[mk]: 195 |
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[bn]: 195 |
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[uk]: 195 |
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[hi]: 195 |
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[ar]: 195 |
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[sq]: 195 |
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[kn]: 195 |
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[tr]: 195 |
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[ca]: 195 |
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[az]: 195 |
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[fa]: 195 |
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[ru]: 195 |
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[mr]: 195 |
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[id]: 195 |
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[no]: 195 |
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[vi]: 195 |
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[th]: 195 |
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[he]: 195 |
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[gu]: 194 |
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[ml]: 194 |
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[ta]: 194 |
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[ur]: 194 |
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[is]: 174 |
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[de]: 154 |
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[da]: 152 |
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[fi]: 146 |
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[el]: 143 |
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[nl]: 142 |
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[fr]: 141 |
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[es]: 140 |
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[cs]: 140 |
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[en]: 139 |
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[lt]: 139 |
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[hu]: 138 |
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[lv]: 137 |
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[pt]: 137 |
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[it]: 136 |
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[et]: 135 |
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[pl]: 135 |
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[sk]: 135 |
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[sl]: 133 |
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[sv]: 130 |
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[bg]: 128 |
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[mt]: 123 |
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[ro]: 122 |
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[hy]: 118 |
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[kk]: 118 |
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[or]: 111 |
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[pa]: 78 |
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[sr]: 74 |
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[as]: 74 |
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[hr]: 72 |
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[ne]: 63 |
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[gl]: 57 |
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[ckb]: 56 |
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[ka]: 56 |
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[ug]: 49 |
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[ky]: 45 |
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[eu]: 35 |
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[ps]: 31 |
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[tl]: 31 |
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[mn]: 29 |
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[fy]: 29 |
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[si]: 24 |
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[nn]: 21 |
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[cy]: 21 |
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[ku]: 19 |
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[tg]: 18 |
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[am]: 14 |
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[tt]: 12 |
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[ja]: 10 |
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[lb]: 9 |
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[tk]: 7 |
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[be]: 6 |
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[my]: 4 |
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[sw]: 4 |
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[af]: 4 |
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[ceb]: 3 |
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[la]: 3 |
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[ba]: 2 |
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[dv]: 2 |
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[eo]: 2 |
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[gd]: 2 |
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[ga]: 2 |
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[mg]: 1 |
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[sd]: 1 |
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[nds]: 1 |
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[yi]: 1 |
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[sah]: 1 |
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</details> |
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### Training procedure |
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GPU: RTX 3090 \ |
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Training time: 1h 53min |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.2833 | 1.0 | 2812 | 0.2004 | 0.94 | |
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| 0.168 | 2.0 | 5625 | 0.1567 | 0.954 | |
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| 0.1131 | 3.0 | 8437 | 0.1429 | 0.9586 | |
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| 0.0832 | 4.0 | 11250 | 0.1257 | 0.967 | |
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| 0.0635 | 5.0 | 14062 | 0.1222 | 0.9682 | |
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| 0.0479 | 6.0 | 16875 | 0.1214 | 0.9704 | |
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| 0.0361 | 7.0 | 19687 | 0.1255 | 0.9712 | |
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| 0.0258 | 8.0 | 22500 | 0.1178 | 0.9712 | |
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| 0.0243 | 9.0 | 25312 | 0.1223 | 0.9724 | |
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| 0.0171 | 10.0 | 28120 | 0.1210 | 0.9721 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
