# Python nlp transformers library understanding the methods/functions/properties **URL:** \ **Category:** Beginners\ **Created:** [December 29, 2021, 11:10pm UTC](https://discuss.huggingface.co/t/python-nlp-transformers-library-understanding-the-methods-functions-properties/13219 "2021-12-29T23:10:15Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![nitempe](https://avatars.discourse-cdn.com/v4/letter/n/54ee81/32.png) [@nitempe](https://discuss.huggingface.co/u/nitempe)\ **Post date:** [December 29, 2021, 11:10pm UTC](https://discuss.huggingface.co/t/python-nlp-transformers-library-understanding-the-methods-functions-properties/13219/1 "2021-12-29T23:10:15Z")
I am learning huggingface transformer model and looked at this [Fine-tuning a pretrained model - Hugging Face Course](https://huggingface.co/course/chapter3/2?fw=tf). I saw some code samples as below and did some additional google search and realized things like below could be done. I decided to explore the documentation and the google search after searching `TFAutoModelForSequenceClassification` returned this [Auto Classes](https://huggingface.co/docs/transformers/model_doc/auto) but that page doesn’t explain methods/functions/properties such as `model.summary()` or `model2.layers[0].trainable`. It doesn’t even mention that there is a parameter `num_labels` in the `TFAutoModelForSequenceClassification.from_pretrained`. I realized these thing because my other google searches returned those Is there any better documentation where I could understand such things? I am finding it incredibly difficult to understand what are the different things that I could do ```auto !pip install datasets transformers[sentencepiece] checkpoint = "bert-base-uncased" from transformers import TFAutoModelForSequenceClassification model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) model.summary() ``` this returns ```auto All model checkpoint layers were used when initializing TFBertForSequenceClassification. Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. Model: "tf_bert_for_sequence_classification_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= bert (TFBertMainLayer) multiple 109482240 dropout_75 (Dropout) multiple 0 classifier (Dense) multiple 1538 ================================================================= Total params: 109,483,778 Trainable params: 109,483,778 Non-trainable params: 0 _________________________________________________________________ ``` Then ```auto # (optional) freeze bert layer model2=model model2.layers[0].trainable = False model2.summary() ``` returns ```auto Model: "tf_bert_for_sequence_classification_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= bert (TFBertMainLayer) multiple 109482240 dropout_75 (Dropout) multiple 0 classifier (Dense) multiple 1538 ================================================================= Total params: 109,483,778 Trainable params: 1,538 Non-trainable params: 109,482,240 _________________________________________________________________ ```