Evaluating a checkpoint
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## Details
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I have trained a NER model with the following command
python3.6 examples/token-classification/run_ner.py --save_steps 100 --data_dir $DIR --model_type bert --model_name_or_path bert-base-cased --output_dir $DIR/model --do_train --do_eval --do_predict --overwrite_output_dir --labels $DIR/labels.txt --evaluate_during_training --max_seq_length 300

Now, I am trying to evaluate and predict at checkpoint 500 (already evaluated during training using --evaluate_during_training argument) using the following command
python3.6 examples/token-classification/run_ner.py --data_dir $DIR --model_type bert --model_name_or_path bert-base-cased --output_dir $DIR/model/checkpoint-500 --do_eval --do_predict --labels $DIR/labels.txt --max_seq_length 300

However, I don't get same performance as during training (as evaluated at the same checkpoint using --evaluate_during_training argument), I get lower performance (by far).

Any idea about it?

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**A link to original question on Stack Overflow**:

Yes: you need to use the path to the saved model as the `--model_name_or_path` argument.

`--model_name_or_path` is the argument that conditions which model is loaded.

Will close this for now.