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--- |
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language: he |
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thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg |
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widget: |
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- text: "עוד בימי קדם" |
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- text: "קוראים לי דורון ואני מעוניין ל" |
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- text: "קוראים לי איציק ואני חושב ש" |
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- text: "החתול שלך מאוד חמוד ו" |
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license: mit |
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--- |
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# hebrew-gpt_neo-small |
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Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. |
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## Datasets |
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1. An assortment of various Hebrew corpuses - I have made it available [here](https://mega.nz/folder/CodSSA4R#4INvMes-56m_WUi7jQMbJQ) |
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2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he) |
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The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. |
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3. CC100-Hebrew Dataset [Homepage](https://metatext.io/datasets/cc100-hebrew) |
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Created by Conneau & Wenzek et al. at 2020, the CC100-Hebrew This dataset is one of the 100 corpora of monolingual data that was processed from the January-December 2018 Commoncrawl snapshots from the CC-Net repository. The size of this corpus is 6.1G., in Hebrew language. |
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## Training Config |
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Available [here](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-small/configs) <BR> |
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## Usage |
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### Google Colab Notebook |
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Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-small/Norod78_hebrew_gpt_neo_small_Colab.ipynb) <BR> |
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#### Simple usage sample code |
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```python |
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!pip install tokenizers==0.10.2 transformers==4.6.0 |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-small") |
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model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-small", pad_token_id=tokenizer.eos_token_id) |
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prompt_text = "אני אוהב שוקולד ועוגות" |
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max_len = 512 |
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sample_output_num = 3 |
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seed = 1000 |
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import numpy as np |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count() |
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print(f"device: {device}, n_gpu: {n_gpu}") |
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np.random.seed(seed) |
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torch.manual_seed(seed) |
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if n_gpu > 0: |
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torch.cuda.manual_seed_all(seed) |
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model.to(device) |
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encoded_prompt = tokenizer.encode( |
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prompt_text, add_special_tokens=False, return_tensors="pt") |
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encoded_prompt = encoded_prompt.to(device) |
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if encoded_prompt.size()[-1] == 0: |
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input_ids = None |
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else: |
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input_ids = encoded_prompt |
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print("input_ids = " + str(input_ids)) |
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if input_ids != None: |
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max_len += len(encoded_prompt[0]) |
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if max_len > 2048: |
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max_len = 2048 |
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print("Updated max_len = " + str(max_len)) |
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stop_token = "<|endoftext|>" |
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new_lines = "\n\n\n" |
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sample_outputs = model.generate( |
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input_ids, |
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do_sample=True, |
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max_length=max_len, |
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top_k=50, |
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top_p=0.95, |
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num_return_sequences=sample_output_num |
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) |
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print(100 * '-' + "\n\t\tOutput\n" + 100 * '-') |
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for i, sample_output in enumerate(sample_outputs): |
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text = tokenizer.decode(sample_output, skip_special_tokens=True) |
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# Remove all text after the stop token |
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text = text[: text.find(stop_token) if stop_token else None] |
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# Remove all text after 3 newlines |
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text = text[: text.find(new_lines) if new_lines else None] |
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print("\n{}: {}".format(i, text)) |
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print("\n" + 100 * '-') |
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``` |
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