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  1. README.md +66 -0
  2. config.json +29 -0
  3. desanka-maksimovic.jpeg +0 -0
  4. merges.txt +0 -0
  5. pytorch_model.bin +3 -0
  6. vocab.json +0 -0
README.md ADDED
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+ ---
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+ language:
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+ - mk
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+ thumbnail: https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg
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+ license: Apache 2.0
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+ datasets:
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+ - wiki-mk
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+ - time-mk-news-2010-2015
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+ ---
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+
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+ # mk-gpt2
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+ Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
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+ Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
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+ [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
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+ and first released at [this page](https://openai.com/blog/better-language-models/).
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+
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+ ## Model description
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+ mk-gpt2 is a transformers model pretrained on a very large corpus of Macedonian data in a self-supervised fashion. This
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+ means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
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+ of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
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+ it was trained to guess the next word in sentences.
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+ More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
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+ shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
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+ predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
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+ This way, the model learns an inner representation of the Macedonian language that can then be used to extract features
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+ useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
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+ prompt.
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+
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+ ### How to use
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+ Here is how to use this model to get the features of a given text in PyTorch:
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+
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+ import random
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+ from transformers import AutoTokenizer, AutoModelWithLMHead
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+
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+ tokenizer = AutoTokenizer.from_pretrained('macedonizer/mk-gpt2') \
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+ model = AutoModelWithLMHead.from_pretrained('macedonizer/mk-gpt2')
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+
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+ input_text = 'Скопје е '
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+
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+ if len(input_text) == 0: \
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+ encoded_input = tokenizer(input_text, return_tensors="pt") \
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+ output = model.generate( \
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+ bos_token_id=random.randint(1, 50000), \
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+ do_sample=True, \
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+ top_k=50, \
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+ max_length=1024, \
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+ top_p=0.95, \
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+ num_return_sequences=1, \
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+ ) \
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+ else: \
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+ encoded_input = tokenizer(input_text, return_tensors="pt") \
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+ output = model.generate( \
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+ **encoded_input, \
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+ bos_token_id=random.randint(1, 50000), \
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+ do_sample=True, \
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+ top_k=50, \
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+ max_length=1024, \
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+ top_p=0.95, \
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+ num_return_sequences=1, \
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+ )
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+
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+ decoded_output = [] \
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+ for sample in output: \
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+ decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
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+
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+ print(decoded_output)
config.json ADDED
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+ {
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+ "activation_function": "gelu_new",
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+ "architectures": [
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+ "GPT2LMHeadModel"
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+ ],
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+ "attn_pdrop": 0.1,
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+ "bos_token_id": 0,
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+ "embd_pdrop": 0.1,
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+ "eos_token_id": 2,
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+ "gradient_checkpointing": false,
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+ "initializer_range": 0.02,
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+ "layer_norm_epsilon": 1e-05,
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+ "model_type": "gpt2",
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+ "n_ctx": 1024,
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+ "n_embd": 768,
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+ "n_head": 12,
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+ "n_inner": null,
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+ "n_layer": 12,
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+ "n_positions": 1024,
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+ "resid_pdrop": 0.1,
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+ "scale_attn_weights": true,
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+ "summary_activation": null,
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+ "summary_first_dropout": 0.1,
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+ "summary_proj_to_labels": true,
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+ "summary_type": "cls_index",
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+ "summary_use_proj": true,
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+ "use_cache": true,
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+ "vocab_size": 52000
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+ }
desanka-maksimovic.jpeg ADDED
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pytorch_model.bin ADDED
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vocab.json ADDED
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