I am using the Huggingface BERTModel, The model gives Seq2SeqModelOutput as output. The output contains the past hidden states and the last hidden state. These are my questions What is the use of the hidden states? How do I pass my hidden states to my output layer? What I actually want is the output tokens, from the model how do I get the prediction tokens?

I am trying to convert pandas dataframe bar plot to matplotlib OO concept. (npy_dataframe["Complaint Type"].value_counts()).head().plot(kind="bar") this live perfectly working and generating bar chat attached now this is my OO matplotlib trying to convert below what is wrong I am doing? %matplotlib inline fig = plt.figure() # creates a figure fig, ax = plt.subplots(figsize=(10,6)) #print(plt.style.available) plt.style.use("seaborn-whitegrid") # most frequest complaint ax.bar(npy_dataframe["Complaint Type"].value_counts().head()) # add some label and title ax.set(title="Most Common Compalints", ylabel="No of Complaints", xlabel="Complaint Type") # Make the legebd visible …

I have a few questions regarding tokenizing word/characters/emojis for different huggingface models. From my understanding, a model would only perform best during inference if the token of the input sentence are within the tokens that the model’s tokenizer was trained on. My questions are: is there a way to easily find out if a particular word/emoji is compatible (included during model training) with the model? (in huggingface context) if this word/emoji is not was not included during model training, what …

Can BART, PEGASUS ... etc. API in huggingface library be used to directly perform multi document summarization? (e.g. here: https://huggingface.co/transformers/model_doc/bart.html)

My task is to generate keywords from sentences. I pretrain a text-generation model. I mask the sentences' tokens and predict the whole sentences' tokens. Pretraining batch_size = 8 and step = 1000000 I haven't observed improvement from pretraining. BLEU score is 10.5 for not pretraining, BLEU score is 9.5 for pretraining. Code I take the python code from https://github.com/google-research/pegasus/blob/master/pegasus/models/transformer.py#L38 hidden_size = 512 num_encoder_layers = 3 num_decoder_layers = 3 Discussion The task is to generate keyword from sentences. The keyword may …