80 lines
2.4 KiB
Markdown
80 lines
2.4 KiB
Markdown
---
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base_model: AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
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language:
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- sv
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- da
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- 'no'
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- en
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pipeline_tag: text-generation
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inference:
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parameters:
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temperature: 0.7
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tags:
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- translation
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---
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# Model Card for gpt-sw3-6.7b-v2-translator
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The `gpt-sw3-6.7b-v2-translator` is a finetuned version of `gpt-sw3-6.7b-v2-instruct` on a carefully selected translation pair dataset that was gathered by AI Sweden.
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## Intended usage:
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Translate text data from English to Swedish, or Swedish to English.
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## How to use:
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```python
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import torch
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from transformers import pipeline, StoppingCriteriaList, StoppingCriteria
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# (Optional) - define a stopping criteria
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# We ideally want the model to stop generate once the response from the Bot is generated
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class StopOnTokenCriteria(StoppingCriteria):
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def __init__(self, stop_token_id):
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self.stop_token_id = stop_token_id
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def __call__(self, input_ids, scores, **kwargs):
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return input_ids[0, -1] == self.stop_token_id
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pipe = pipeline(
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task="text-generation",
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model="AI-Sweden-Models/gpt-sw3-6.7b-v2-translator",
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device=device
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)
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stop_on_token_criteria = StopOnTokenCriteria(stop_token_id=pipe.tokenizer.bos_token_id)
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text = "I like to eat ice cream in the summer."
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# This will translate English to Swedish
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# To translate from Swedish to English the prompt would be:
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# prompt = f"<|endoftext|><s>User: Översätt till Engelska från Svenska\n{text}<s>Bot:"
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prompt = f"<|endoftext|><s>User: Översätt till Svenska från Engelska\n{text}<s>Bot:"
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input_tokens = pipe.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
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max_model_length = 2048
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dynamic_max_length = max_model_length - input_tokens.shape[1]
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response = pipe(
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prompt,
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max_length=dynamic_max_length,
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truncation=True,
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stopping_criteria=StoppingCriteriaList([stop_on_token_criteria])
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)
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print(response[0]["generated_text"].split("<s>Bot: ")[-1])
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```
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```python
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>>> "Jag tycker om att äta glass på sommaren."
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```
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## Training & Data:
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The training was done on 1 NVIDIA DGX using DeepSpeed ZeRO 3 for three epochs on roughly 4GB of carefully selected translation data. It is a full finetune of all of the model parameters.
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| Epoch | Training Loss | Evaluation Loss |
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|-------|---------------|-----------------|
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| 1 | 1.309 | 1.281 |
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| 2 | 1.161 | 1.242 |
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| 3 | 1.053 | 1.219 | |