Inside Pipeline Tf

[{'label': 'POSITIVE', 'score': 0.9598047137260437},
, {'label': 'NEGATIVE', 'score': 0.9994558095932007}]

{'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=
array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,
        12172,  2607,  2026,  2878,  2166,  1012,   102],
       [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,
            0,     0,     0,     0,     0,     0,     0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>}

Some layers from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english were not used when initializing TFDistilBertModel: ['pre_classifier', 'classifier', 'dropout_19']
- This IS expected if you are initializing TFDistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing TFDistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
All the layers of TFDistilBertModel were initialized from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertModel for predictions without further training.

(2, 16, 768)

Some layers from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english were not used when initializing TFDistilBertForSequenceClassification: ['dropout_19']
- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english and are newly initialized: ['dropout_57']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

tf.Tensor(
[[-1.5606967  1.6122819]
 [ 4.169231  -3.3464472]], shape=(2, 2), dtype=float32)

tf.Tensor(
[[4.0195346e-02 9.5980465e-01]
 [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)

{0: 'NEGATIVE', 1: 'POSITIVE'}