AutoModelForSequenceClassification is a general-purpose class for sequence classification tasks, not just for sentiment analysis. Sequence classification is a broader category that includes sentiment analysis, as well as other tasks like spam detection, topic classification, and more. It is a versatile class in the Hugging Face Transformers library that allows you to use pre-trained models for any sequence classification task.
When using a pre-trained model fine-tuned for sentiment analysis with AutoModelForSequenceClassification, the model will output logits corresponding to sentiment classes. In this specific case, you have three classes (negative, neutral, and positive), so the model outputs logits for each class. The line predicted_class = outputs.logits.argmax(dim=-1).item() calculates the index of the highest logit (most probable class), which represents the sentiment class.
However, keep in mind that this behavior depends on the pre-trained model you use. If you use a model fine-tuned for another sequence classification task, it might output a different number of classes, and the interpretation of the results would also be different. Always make sure to use a model that is fine-tuned for the specific task you're interested in.
IN OTHER WORDS:
Yes, AutoModelForSequenceClassification sets up the model to output logits, and the meaning of these logits depends on the specific pre-trained model and the task it has been fine-tuned for.
The number of output logits is not always three. It depends on the specific model and the number of classes in the sequence classification task it was fine-tuned for. For example, in the sentiment analysis example you provided, there are three sentiment classes (negative, neutral, and positive), so the model outputs three logits.
However, if you were using a model fine-tuned for another task with a different number of classes, the output would have a different number of logits. For instance, if you were using a model for topic classification with five possible topics, the model would output five logits, one for each topic. The number of logits corresponds to the number of classes in the classification task the model was fine-tuned for.
The output you're seeing is a SequenceClassifierOutput object, which is a named tuple containing the following attributes:
loss: This value represents the loss (a scalar value) calculated during the training phase. It is used to update the model's weights. In the inference phase (i.e., when making predictions), the loss is typically None.
logits: These are the raw, unnormalized scores output by the model for each class. In your case, the tensor has the shape (1, 3), which means there is 1 input sample and 3 classes. The logits tensor tensor([[-2.0170, -1.5858, 3.8066]], grad_fn=) contains the scores for each class. The higher the score, the more likely the model thinks that the input belongs to that class.
hidden_states: This attribute contains the hidden states of the model's layers, which can be useful for certain tasks like feature extraction. It is typically set to None when you're only interested in the final output of the model, like in your case.
attentions: This attribute contains the attention weights for each layer of the model. These weights can be useful for visualizing and understanding the model's inner workings. Like hidden states, attentions are typically set to None when you're only interested in the final output of the model.
For your specific use case, you're mainly interested in the logits tensor. You can access it by calling outputs.logits. The highest logit value in the tensor indicates the most likely class for the input text. You can find the index of the highest logit using predicted_class = outputs.logits.argmax(dim=-1).item(). This index can then be used to map to the corresponding sentiment label (e.g., negative, neutral, or positive)