Hermi2023 commited on
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c52e424
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Update README.md

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I don't know where this model came, I didn't create one like this. Corrected the model URL.

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  1. README.md +3 -3
README.md CHANGED
@@ -24,7 +24,7 @@ You can then generate text as follows:
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  ```python
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  from transformers import pipeline
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- generator = pipeline("text-generation", model="Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmp5nx4qcp3/Hermi2023/doc2query-ppo-msmarco-43520-121")
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  outputs = generator("Hello, my llama is cute")
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  ```
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@@ -34,8 +34,8 @@ If you want to use the model for training or to obtain the outputs from the valu
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  from transformers import AutoTokenizer
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  from trl import AutoModelForCausalLMWithValueHead
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- tokenizer = AutoTokenizer.from_pretrained("Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmp5nx4qcp3/Hermi2023/doc2query-ppo-msmarco-43520-121")
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- model = AutoModelForCausalLMWithValueHead.from_pretrained("Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmp5nx4qcp3/Hermi2023/doc2query-ppo-msmarco-43520-121")
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  inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
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  outputs = model(**inputs, labels=inputs["input_ids"])
 
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  ```python
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  from transformers import pipeline
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+ generator = pipeline("text-generation", model="Hermi2023/doc2query-ppo-msmarco-43520-121")
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  outputs = generator("Hello, my llama is cute")
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  ```
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  from transformers import AutoTokenizer
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  from trl import AutoModelForCausalLMWithValueHead
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+ tokenizer = AutoTokenizer.from_pretrained("Hermi2023/doc2query-ppo-msmarco-43520-121")
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+ model = AutoModelForCausalLMWithValueHead.from_pretrained("Hermi2023/doc2query-ppo-msmarco-43520-121")
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  inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
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  outputs = model(**inputs, labels=inputs["input_ids"])