For a 6 class sentence classification task, I have a list of sentences where I retrieve the absolute values before the softmax is applied. Example list of sentences:
s = ['I like the weather today', 'The movie was very scary', 'Love is in the air']
I get the values the following way:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "Emanuel/bertweet-emotion-base"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
for i in s:
sentence = tokenizer(i, return_tensors="pt")
output = model(sentence["input_ids"])
print(output.logits.detach().numpy())
# returns [[-0.8390876 2.9480567 -0.5134539 0.70386493 -0.5019671 -2.619496 ]]
#[[-0.8847909 -0.9642067 -2.2108874 -0.43932158 4.3386173 -0.37383893]]
#[[-0.48750368 3.2949197 2.1660519 -0.6453249 -1.7101991 -2.817954 ]]
How do I create a data frame with columns sentence, class_1, class_2, class_3, class_4, class_5, class_6 where I add values iteratively or maybe in a more optimal way where I append each new sentence and its absolute values? What would be the best way?
Expected output:
sentence class_1 class_2 class_3 ....
0 I like the weather today -0.8390876 2.9480567 -0.5134539 ....
1 The movie was very scary -0.8847909 -0.9642067 -2.2108874 ....
2 Love is in the air -0.48750368 3.2949197 2.1660519 ....
...
If I only had one sentence, I could transform it to a data frame like this, but I would still need to append the sentence somehow
sentence = tokenizer("Love is in the air", return_tensors="pt")
output = model(sentence["input_ids"])
px = pd.DataFrame(output.logits.detach().numpy())
Maybe creating two separate data frames and then appending them would be one plausible way of doing this?
I managed to come up with a solution and I am posting it as someone might find it useful.
The idea is to initialize a data frame and to append the absolute values for every sentence while iterating
absolute_vals = pd.DataFrame()
for i in s:
sentence = tokenizer(i, return_tensors="pt")
output = model(sentence["input_ids"])
px = pd.DataFrame(output.logits.detach().numpy())
absolute_vals = absolute_vals.append(px, ignore_index = True)
absolute_vals
Returns:
sentence class_1 class_2 class_3 ....
0 I like the weather today -0.8390876 2.9480567 -0.5134539 ....
1 The movie was very scary -0.8847909 -0.9642067 -2.2108874 ....
2 Love is in the air -0.48750368 3.2949197 2.1660519 ....
...