Update README.md

README.md

@@ -6,13 +6,14 @@ tags:
 datasets:
 - imdb
 model-index:
-- name: distilbert-base-uncased-finetuned-imdb-v2
+- name: distilbert-base-uncased-finetuned-imdb
   results: []
+language:
+- en
+metrics:
+- perplexity
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # distilbert-base-uncased-finetuned-imdb-v2
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
@@ -21,15 +22,15 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is a fine-tuned version of DistilBERT base uncased on the IMDb dataset. It was trained to predict the next word in a sentence using masked language modeling. The model has been fine-tuned to adapt to the language patterns and sentiment present in movie reviews.
 
 ## Intended uses & limitations
 
-More information needed
+This model is primarily designed for the fill-mask task, a type of language modeling where the model is trained to predict missing words within a given context. It excels at completing sentences or phrases by predicting the most likely missing word based on the surrounding text. This functionality makes it valuable for a wide range of natural language processing tasks, such as generating coherent text, improving auto-completion in writing applications, and enhancing conversational agents' responses. However, it may have limitations in handling domain-specific language or topics not present in the IMDb dataset. Additionally, it may not perform well on languages other than English.
 
 ## Training and evaluation data
 
-More information needed
+The model was trained on a subset of the IMDb dataset, containing 40,000 reviews for fine-tuning. The evaluation was conducted on a separate test set of 6,000 reviews.
 
 ## Training procedure
 
@@ -59,3 +60,48 @@ The following hyperparameters were used during training:
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.4
 - Tokenizers 0.13.3
+
+## How to use
+
+```python
+import torch
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Francesco-A/distilbert-base-uncased-finetuned-imdb-v2")
+model = AutoModelForMaskedLM.from_pretrained("Francesco-A/distilbert-base-uncased-finetuned-imdb-v2")
+
+# Example sentence
+sentence = "This movie is really [MASK]."
+
+# Tokenize the sentence
+inputs = tokenizer(sentence, return_tensors="pt")
+
+# Get the model's predictions
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get the top-k predicted tokens and their probabilities
+k = 5  # Number of top predictions to retrieve
+masked_token_index = inputs["input_ids"].tolist()[0].index(tokenizer.mask_token_id)
+predicted_token_logits = outputs.logits[0, masked_token_index]
+topk_values, topk_indices = torch.topk(torch.softmax(predicted_token_logits, dim=-1), k)
+
+# Convert top predicted token indices to words
+predicted_tokens = [tokenizer.decode(idx.item()) for idx in topk_indices]
+# Convert probabilities to Python floats
+probs = topk_values.tolist()
+
+# Create a DataFrame to display the top predicted words and probabilities
+data = {
+    "Predicted Words": predicted_tokens,
+    "Probability": probs,
+}
+
+df = pd.DataFrame(data)
+
+# Display the DataFrame
+df
+
+```