Safetensors
Chinese
Catalan
m2m_100
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ datasets:
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+ - projecte-aina/CA-ZH_Parallel_Corpus
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+ language:
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+ - zh
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+ - ca
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+ base_model:
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+ - facebook/m2m100_1.2B
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  ---
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+ ## Projecte Aina’s Catalan-Chinese machine translation model
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+
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+ ## Table of Contents
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+ <details>
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+ <summary>Click to expand</summary>
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+
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+ - [Model description](#model-description)
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+ - [Intended uses and limitations](#intended-uses-and-limitations)
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+ - [How to use](#how-to-use)
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+ - [Limitations and bias](#limitations-and-bias)
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+ - [Training](#training)
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+ - [Evaluation](#evaluation)
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+ - [Additional information](#additional-information)
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+
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+ </details>
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+
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+
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+ ## Model description
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+
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+ This machine translation model is built upon the M2M100 1.2B, fine-tuned specifically for Catalan-Chinese translation.
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+ It is trained on a combination of Catalan-Chinese datasets
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+ totalling 94,187,858 sentence pairs. 113,305 sentence pairs were parallel data collected from the web, while the remaining 94,074,553 sentence pairs
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+ were parallel synthetic data created using the
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+ [Aina Project's Spanish-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-es-ca) and the [Aina Project's English-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-en-ca).
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+
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+ Following the fine-tuning phase, Contrastive Preference Optimization (CPO) was applied to further refine the model's outputs. CPO training involved pairs of "chosen" and "rejected" translations for a total of 4,006 sentences. These sentences were sourced from the Flores development set (997 sentences), the Flores devtest set (1,012 sentences), and the NTREX set (1,997 sentences).
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+
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+ The model was evaluated on the Projecte Aina's Catalan-Chinese evaluation dataset, which contains 1022 sentences.
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+
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+ ## Intended uses and limitations
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+
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+ You can use this model for machine translation from Catalan to simplified Chinese.
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+
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+ ## How to use
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+
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+ ### Usage
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+
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+ Translate a sentence using python
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+
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+ model_id = "projecte-aina/aina-translator-ca-zh-v2"
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+
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+ model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+
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+ sentence = "Benvingut al projecte Aina!"
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+
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+ input_ids = tokenizer(sentence, return_tensors="pt").input_ids
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+ output_ids = model.generate(input_ids, max_length=200, num_beams=5)
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+
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+ generated_translation= tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
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+ print(generated_translation)
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+ #欢迎来到 Aina 项目!
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+ ```
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+
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+
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+ ## Limitations and bias
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+ At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model.
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+ However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
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+
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+ ## Training
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+
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+ ### Training data
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+
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+ The Catalan-Chinese data collected from the web was a combination of the following datasets:
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+
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+ | Dataset | Sentences before cleaning |
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+ |-------------------|----------------|
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+ | OpenSubtitles | 139.300 |
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+ | WikiMatrix | 90.643 |
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+ | Wikipedia | 68.623|
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+ | **Total** | **298.566** |
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+
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+ 94.074.553 sentence pairs of synthetic parallel data were created from the following Spanish-Chinese datasets and English-Chinese datasets:
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+
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+ **Spanish-Chinese:**
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+
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+ | Dataset | Sentences before cleaning |
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+ |-------------------|----------------|
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+ | NLLB |24.051.233|
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+ | UNPC | 17.599.223 |
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+ | MultiUN | 9.847.770 |
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+ | OpenSubtitles | 9.319.658 |
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+ | MultiParaCrawl | 3.410.087 |
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+ | MultiCCAligned | 3.006.694 |
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+ | WikiMatrix | 1.214.322 |
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+ | News Commentary | 375.982 |
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+ | Tatoeba | 9.404 |
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+ | **Total** | **68.834.373** |
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+
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+ **English-Chinese:**
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+
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+ | Dataset | Sentences before cleaning |
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+ |-------------------|----------------|
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+ | NLLB |71.383.325|
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+ | CCAligned | 15.181.415 |
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+ | Paracrawl | 14.170.869|
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+ | WikiMatrix | 2.595.119|
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+ | **Total** | **103.330.728** |
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+
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+
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+
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+ ### Training procedure
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+
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+ ### Data preparation
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+
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+ **Catalan-Chinese parallel data**
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+
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+ The Chinese side of all datasets were first processed using the [Hanzi Identifier](https://github.com/tsroten/hanzidentifier) to detect Traditional Chinese, which was subsequently converted to Simplified Chinese using [OpenCC](https://github.com/BYVoid/OpenCC).
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+
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+ All data was then filtered according to two specific criteria:
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+
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+ - Alignment: sentence level alignments were calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) and sentence pairs with a score below 0.75 were discarded.
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+
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+ - Language identification: the probability of being the target language was calculated using [Lingua.py](https://github.com/pemistahl/lingua-py) and sentences with a language probability score below 0.5 were discarded.
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+
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+ Next, Spanish data was translated into Catalan using the Aina Project's [Spanish-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-es-ca), while English data was translated into Catalan using the Aina Project's [English-Catalan machine translation model](https://huggingface.co/projecte-aina/aina-translator-en-ca).
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+
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+ The filtered and translated datasets are then concatenated and deduplicated to form a final corpus of 94.187.858.
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+
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+ **Catalan-Chinese Contrastive Preference Optimization dataset**
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+
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+ The CPO dataset is built by comparing the quality of translations across four distinct sources:
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+
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+ - Reference translation: Chinese sentences from Flores test set, Flores devtest set, and NTREX dataset.
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+ - [aina-translator-ca-zh](https://huggingface.co/projecte-aina/aina-translator-ca-zh): A specialized bilingual model for Catalan-Chinese translations.
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+ - Google Translate: A widely-used general-purpose machine translation system.
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+ - OpenAI GPT-4: A large-scale language model capable of performing a wide range of tasks in conversational settings, including high-quality translation.
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+
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+ To evaluate the quality of translations without relying on human annotations, we employ two reference-free evaluation models:
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+
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+ - [Unbabel/wmt23-cometkiwi-da-xxl](https://huggingface.co/Unbabel/wmt23-cometkiwi-da-xxl)
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+ - [Unbabel/XCOMET-XXL](https://huggingface.co/Unbabel/XCOMET-XXL)
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+
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+ These models provide direct assessment scores for each translation. The scores from both models are averaged to determine the relative quality of each translation. Based on this evaluation, the highest-scoring ("chosen") and lowest-scoring ("rejected") translations are identified for each source sentence, forming contrastive pairs. The CPO dataset comprises a total of 4,006 such pairs of "chosen" and "rejected" translations.
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+
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+
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+ #### Training
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+
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+ The training was executed on NVIDIA GPUs utilizing the Hugging Face Transformers framework.
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+ The model was trained for 245.000 updates.
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+
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+ Following fine-tuning on the M2M100 1.2B model, Contrastive Preference Optimization (CPO) was performed using our CPO dataset and the Hugging Face CPO Trainer. This phase involved 1,500 updates.
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+
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+ ## Evaluation
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+
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+ ### Variable and metrics
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+
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+ Below are the evaluation results on the Projecte Aina's Catalan-Chinese test set, compared to Google Translate for the CA-ZH direction. The evaluation was conducted using [`tower-eval`](https://github.com/deep-spin/tower-eval) following the standard setting (beam search with beam size 5, limiting the translation length to 200 tokens). We report the following metrics:
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+
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+ - BLEU: Sacrebleu implementation, version:2.4.0
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+ - ChrF: Sacrebleu implementation.
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+ - Comet: Model checkpoint: "Unbabel/wmt22-comet-da".
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+ - Comet-kiwi: Model checkpoint: "Unbabel/wmt22-cometkiwi-da".
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+
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+
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+ ### Evaluation results
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+
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+ Below are the evaluation results on the machine translation from Chinese to Catalan compared to [Google Translate](https://translate.google.com/):
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+
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+
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+ #### Projecte Aina's Catalan-Chinese evaluation dataset
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+
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+ | | Bleu ↑ | ChrF ↑ | Comet ↑ | Comet-kiwi ↑ |
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+ |:-----------------------|-------:|------:|-------:|--------:|-------------:|---------:|
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+ | aina-translator-zh-ca-v2 | **28.55** | **57.64** | **0.87** | **0.82** |
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+ | Google Translate | 26.84 | 55.7 | 0.86 | **0.82** |
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+
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+
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+
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+ ## Additional information
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+
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+ ### Author
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+ The Language Technologies Unit from Barcelona Supercomputing Center.
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+
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+ ### Contact
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+ For further information, please send an email to <[email protected]>.
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+
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+ ### Copyright
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+ Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
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+
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+ ### License
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+ [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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+
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+ ### Funding
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+ This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
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+
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+ ### Disclaimer
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+
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+ <details>
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+ <summary>Click to expand</summary>
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+
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+ The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.
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+
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+ Be aware that the model may have biases and/or any other undesirable distortions.
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+
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+ When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it)
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+ or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and,
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+ in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
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+
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+ In no event shall the owner and creator of the model (Barcelona Supercomputing Center)
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+ be liable for any results arising from the use made by third parties.
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+
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+ </details>
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+ "__hr__",
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+ "__ht__",
38
+ "__hu__",
39
+ "__hy__",
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+ "__id__",
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+ "__ig__",
42
+ "__ilo__",
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+ "__is__",
44
+ "__it__",
45
+ "__ja__",
46
+ "__jv__",
47
+ "__ka__",
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+ "__kk__",
49
+ "__km__",
50
+ "__kn__",
51
+ "__ko__",
52
+ "__lb__",
53
+ "__lg__",
54
+ "__ln__",
55
+ "__lo__",
56
+ "__lt__",
57
+ "__lv__",
58
+ "__mg__",
59
+ "__mk__",
60
+ "__ml__",
61
+ "__mn__",
62
+ "__mr__",
63
+ "__ms__",
64
+ "__my__",
65
+ "__ne__",
66
+ "__nl__",
67
+ "__no__",
68
+ "__ns__",
69
+ "__oc__",
70
+ "__or__",
71
+ "__pa__",
72
+ "__pl__",
73
+ "__ps__",
74
+ "__pt__",
75
+ "__ro__",
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+ "__ru__",
77
+ "__sd__",
78
+ "__si__",
79
+ "__sk__",
80
+ "__sl__",
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+ "__so__",
82
+ "__sq__",
83
+ "__sr__",
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+ "__ss__",
85
+ "__su__",
86
+ "__sv__",
87
+ "__sw__",
88
+ "__ta__",
89
+ "__th__",
90
+ "__tl__",
91
+ "__tn__",
92
+ "__tr__",
93
+ "__uk__",
94
+ "__ur__",
95
+ "__uz__",
96
+ "__vi__",
97
+ "__wo__",
98
+ "__xh__",
99
+ "__yi__",
100
+ "__yo__",
101
+ "__zh__",
102
+ "__zu__"
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+ ],
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+ "bos_token": {
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+ "single_word": false
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+ },
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+ "pad_token": {
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+ "single_word": false
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+ },
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+ "unk_token": {
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
138
+ }
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+ }
tokenizer_config.json ADDED
@@ -0,0 +1,1032 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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551
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552
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+ "special": true
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+ "special": true
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580
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581
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582
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583
+ "rstrip": false,
584
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585
+ "special": true
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588
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592
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593
+ "special": true
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596
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597
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599
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600
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601
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+ "special": true
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615
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628
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629
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631
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639
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644
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645
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646
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647
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648
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652
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655
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656
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657
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660
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661
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662
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663
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664
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665
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672
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673
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678
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679
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680
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681
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+ "128085": {
684
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685
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689
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+ "special": true
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+ "special": true
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+ "special": true
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799
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800
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801
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804
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805
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806
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807
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808
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809
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+ "128101": {
812
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814
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815
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816
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817
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+ "128102": {
820
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821
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822
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824
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825
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827
+ "128103": {
828
+ "content": "__zu__",
829
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831
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832
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833
+ "special": true
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+ "128104": {
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+ "128105": {
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846
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847
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848
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855
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856
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857
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859
+ "128107": {
860
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861
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862
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863
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864
+ "single_word": false,
865
+ "special": false
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867
+ "128108": {
868
+ "content": "!",
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870
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872
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873
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875
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878
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879
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880
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881
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894
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+ },
916
+ "additional_special_tokens": [
917
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918
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919
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920
+ "__ast__",
921
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922
+ "__ba__",
923
+ "__be__",
924
+ "__bg__",
925
+ "__bn__",
926
+ "__br__",
927
+ "__bs__",
928
+ "__ca__",
929
+ "__ceb__",
930
+ "__cs__",
931
+ "__cy__",
932
+ "__da__",
933
+ "__de__",
934
+ "__el__",
935
+ "__en__",
936
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937
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938
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939
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940
+ "__fi__",
941
+ "__fr__",
942
+ "__fy__",
943
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944
+ "__gd__",
945
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946
+ "__gu__",
947
+ "__ha__",
948
+ "__he__",
949
+ "__hi__",
950
+ "__hr__",
951
+ "__ht__",
952
+ "__hu__",
953
+ "__hy__",
954
+ "__id__",
955
+ "__ig__",
956
+ "__ilo__",
957
+ "__is__",
958
+ "__it__",
959
+ "__ja__",
960
+ "__jv__",
961
+ "__ka__",
962
+ "__kk__",
963
+ "__km__",
964
+ "__kn__",
965
+ "__ko__",
966
+ "__lb__",
967
+ "__lg__",
968
+ "__ln__",
969
+ "__lo__",
970
+ "__lt__",
971
+ "__lv__",
972
+ "__mg__",
973
+ "__mk__",
974
+ "__ml__",
975
+ "__mn__",
976
+ "__mr__",
977
+ "__ms__",
978
+ "__my__",
979
+ "__ne__",
980
+ "__nl__",
981
+ "__no__",
982
+ "__ns__",
983
+ "__oc__",
984
+ "__or__",
985
+ "__pa__",
986
+ "__pl__",
987
+ "__ps__",
988
+ "__pt__",
989
+ "__ro__",
990
+ "__ru__",
991
+ "__sd__",
992
+ "__si__",
993
+ "__sk__",
994
+ "__sl__",
995
+ "__so__",
996
+ "__sq__",
997
+ "__sr__",
998
+ "__ss__",
999
+ "__su__",
1000
+ "__sv__",
1001
+ "__sw__",
1002
+ "__ta__",
1003
+ "__th__",
1004
+ "__tl__",
1005
+ "__tn__",
1006
+ "__tr__",
1007
+ "__uk__",
1008
+ "__ur__",
1009
+ "__uz__",
1010
+ "__vi__",
1011
+ "__wo__",
1012
+ "__xh__",
1013
+ "__yi__",
1014
+ "__yo__",
1015
+ "__zh__",
1016
+ "__zu__"
1017
+ ],
1018
+ "bos_token": "<s>",
1019
+ "clean_up_tokenization_spaces": true,
1020
+ "eos_token": "</s>",
1021
+ "language_codes": "m2m100",
1022
+ "model_max_length": 1000000000000000019884624838656,
1023
+ "num_madeup_words": 8,
1024
+ "pad_token": "<pad>",
1025
+ "sep_token": "</s>",
1026
+ "sp_model_kwargs": {},
1027
+ "src_lang": "ca",
1028
+ "tgt_lang": "zh",
1029
+ "tokenizer_class": "M2M100Tokenizer",
1030
+ "unk_token": "<unk>",
1031
+ "use_fast": false
1032
+ }
vocab.json ADDED
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