# Am I doing this right?

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**Category:** Beginners\
**Created:** [July 11, 2020, 1:07am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fam-i-doing-this-right%2F207 "2020-07-11T01:07:36Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![pwasoutside](https://avatars.discourse-cdn.com/v4/letter/p/848f3c/32.png) [@pwasoutside](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fpwasoutside)%5C
**Post date:** [July 11, 2020, 1:07am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fam-i-doing-this-right%2F207%2F1 "2020-07-11T01:07:36Z")

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Fairly new to ML and very new to transformers. Want to make sure I’m doing the right thing … I’m trying to do text classification with a small data set and though this would be a good option (is it?)

Here’s the basics of my code:

```
texts = ["random text string...", ...]
labels = [1, 0, ...]

tokenized_sents = []
attention_masks = []

for sentence in sentences:
    tokenized_sents.append(tokenizer.encode(sentence, add_special_tokens=True, ...))
input_ids = pad_sequences(tokenized_sents)

for sentence in input_ids:
  att_mask = [int(token_id > 0) for token_id in sentence]
  attention_masks.append(att_mask)

dataset = tf.data.Dataset.from_tensor_slices(input_ids, attention_masks, labels)

# copied from https://huggingface.co/transformers/training.html
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss)
model.fit(dataset, epochs=2, steps_per_epoch=115)

```

I was pretty confident this all worked, but then when I did the following test:

```
sent = ["I like to watch movies"]
sent = tokenizer.encode(sentence, add_special_tokens=True, ...)
att_mask = [int(token_id > 0) for token_id in sent]
ds = tf.data.Dataset.from_tensor_slices(sent, att_mask)
model.predict(ds)

```

I got a super long array. But the labels can only be 1 or 0 and there’s only one sample, so I was expecting a 1 by 2 array. Any idea why this doesn’t work?  
Also, what’s the best way to save this model and use it for predictions later.  
Thank you.

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<div class="post-metadata">

**Author:** ![leoapolonio](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/leoapolonio/32/28_2.png) [@leoapolonio](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fleoapolonio)%5C
**Post date:** [July 12, 2020, 9:11pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fam-i-doing-this-right%2F207%2F2 "2020-07-12T21:11:17Z")

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I would suggest expanding this example instead: [https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01\_text\_classification.ipynb](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)