thefrigidliquidation
commited on
Commit
•
347a529
1
Parent(s):
9f97b8a
Further trained model
Browse files- README.md +45 -1
- added_tokens.json +1039 -0
- config.json +3 -3
- pytorch_model.bin +2 -2
- tokenizer.json +2 -2
- tokenizer_config.json +2 -2
README.md
CHANGED
@@ -11,4 +11,48 @@ license: cc-by-nc-4.0
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This model was fine-tuned on light and web novel for Japanese to English translation.
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This model was fine-tuned on light and web novel for Japanese to English translation.
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It can translate sentences and paragraphs up to 512 tokens.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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tokenizer = AutoTokenizer.from_pretrained("thefrigidliquidation/nllb-jaen-1.3B-lightnovels")
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model = AutoModelForSeq2SeqLM.from_pretrained("thefrigidliquidation/nllb-jaen-1.3B-lightnovels")
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generated_tokens = model.generate(
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**inputs,
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forced_bos_token_id=tokenizer.lang_code_to_id[tokenizer.tgt_lang],
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max_new_tokens=1024,
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no_repeat_ngram_size=6,
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).cpu()
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translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
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```
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Generating with diverse beam search seems to work best. Add the following to `model.generate`:
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```python
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num_beams=8,
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num_beam_groups=4,
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do_sample=False,
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```
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## Glossary
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You can provide up to 10 custom translations for nouns and character names at runtime. To do so, surround the Japanese term with term tokens. Prefix the word with one of <t0>, <t1>, ..., <t9> and suffix the word with </t>. The term will be translated as the prefix term token which can then be string replaced.
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For example, in "マイン、ルッツが迎えに来たよ" if you wish to have "マイン" translated as "Myne" you would replace "マイン" with "<t0>マイン</t>". The model will translate "<t0>マイン</t>、ルッツが迎えに来たよ" as "<t0>, Lutz is here to pick you up." Then simply do a string replacement on the output, replacing "<t0>" with "Myne".
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## Honorifics
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You can force the model to generate or ignore honorifics.
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```python
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# default, the model decides whether to use honorifics
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tokenizer.tgt_lang = "jpn_Jpan"
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# no honorifics, the model is discouraged from using honorifics
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tokenizer.tgt_lang = "zsm_Latn"
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# honorifics, the model is encouraged to use honorifics
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tokenizer.tgt_lang = "zul_Latn"
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```
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added_tokens.json
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@@ -0,0 +1,1039 @@
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1 |
+
{
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2 |
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"</fs>": 257229,
|
3 |
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"</t>": 257230,
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4 |
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"<fs>": 257228,
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5 |
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"<t0>": 257231,
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6 |
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"<t1>": 257232,
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7 |
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"<t2>": 257233,
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8 |
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"<t3>": 257234,
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9 |
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"<t4>": 257235,
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"<t5>": 257236,
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"<t6>": 257237,
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"<t7>": 257238,
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"<t8>": 257239,
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"<t9>": 257240,
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"⦅": 257198,
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"〆": 256692,
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"〒": 256881,
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18 |
+
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config.json
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{
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@@ -28,7 +28,7 @@
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|
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pytorch_model.bin
CHANGED
@@ -1,3 +1,3 @@
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size 5495548056
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tokenizer.json
CHANGED
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tokenizer_config.json
CHANGED
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