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README.md
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@@ -38,7 +38,6 @@ int2str = {
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str2int = {v:k for k,v in int2str.items()}
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# Load model dependencies
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-
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model = AutoModelForTokenClassification.from_pretrained(
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"brolaurens/finer-distilbert", num_labels=len(int2str), id2label=int2str, label2id=str2int
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)
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predictions = model(**model_input).logits
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predictions = predictions.argmax(axis=2)
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predicted_labels = [[int2str[x] for x in t] for t in predictions.tolist()]
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-
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str2int = {v:k for k,v in int2str.items()}
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# Load model dependencies
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model = AutoModelForTokenClassification.from_pretrained(
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"brolaurens/finer-distilbert", num_labels=len(int2str), id2label=int2str, label2id=str2int
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)
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predictions = model(**model_input).logits
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predictions = predictions.argmax(axis=2)
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predicted_labels = [[int2str[x] for x in t] for t in predictions.tolist()]
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```
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## Training parameters
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The model was trained using the following hyperparameters:
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base_model: distilbert/distilbert-base-uncased
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learning_rate: 2e-5
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batch_size: 32
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epochs: 3
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optimizer: adamw
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adam_beta1: 0.9
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adam_beta2: 0.999
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adam_epsilon: 1e-08
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loss_function: cross entropy loss
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