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README.md
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## Training procedure
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CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
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The family of models are trained using
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See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
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## Evaluation results
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained(
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model = AutoModelForCausalLM.from_pretrained(
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text = "def hello_world():"
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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## Training procedure
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CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
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The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism.
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See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
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## Evaluation results
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-nl")
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model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-nl")
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text = "def hello_world():"
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input_ids = tokenizer(text, return_tensors="pt").input_ids
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