Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/neuraly/bert-base-italian-cased-sentiment/README.md

README.md

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+---
+language: it
+thumbnail: https://neuraly.ai/static/assets/images/huggingface/thumbnail.png
+tags:
+- sentiment
+- Italian
+license: MIT
+widget:
+- text: "Huggingface è un team fantastico!"
+---
+
+# 🤗 + neuraly - Italian BERT Sentiment model
+  
+## Model description  
+  
+This model performs sentiment analysis on Italian sentences. It was trained starting from an instance of [bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased), and fine-tuned on an Italian dataset of tweets, reaching 82% of accuracy on the latter one.
+  
+## Intended uses & limitations  
+  
+#### How to use  
+  
+```python
+import torch
+from torch import nn  
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("neuraly/bert-base-italian-cased-sentiment")
+# Load the model, use .cuda() to load it on the GPU
+model = AutoModelForSequenceClassification.from_pretrained("neuraly/bert-base-italian-cased-sentiment")
+
+sentence = 'Huggingface è un team fantastico!'
+input_ids = tokenizer.encode(sentence, add_special_tokens=True)
+
+# Create tensor, use .cuda() to transfer the tensor to GPU
+tensor = torch.tensor(input_ids).long()
+# Fake batch dimension
+tensor = tensor.unsqueeze(0)
+
+# Call the model and get the logits
+logits, = model(tensor)
+
+# Remove the fake batch dimension
+logits = logits.squeeze(0)
+
+# The model was trained with a Log Likelyhood + Softmax combined loss, hence to extract probabilities we need a softmax on top of the logits tensor
+proba = nn.functional.softmax(logits, dim=0)
+
+# Unpack the tensor to obtain negative, neutral and positive probabilities
+negative, neutral, positive = proba
+```  
+  
+#### Limitations and bias  
+  
+A possible drawback (or bias) of this model is related to the fact that it was trained on a tweet dataset, with all the limitations that come with it. The domain is strongly related to football players and teams, but it works surprisingly well even on other topics.
+  
+## Training data  
+  
+We trained the model by combining the two tweet datasets taken from [Sentipolc EVALITA 2016](http://www.di.unito.it/~tutreeb/sentipolc-evalita16/data.html).  Overall the dataset consists of  45K pre-processed tweets.
+
+The model weights come from a pre-trained instance of [bert-base-italian-cased](https://huggingface.co/dbmdz/bert-base-italian-cased). A huge "thank you" goes to that team, brilliant work!
+  
+## Training procedure  
+  
+#### Preprocessing
+
+We tried to save as much information as possible, since BERT captures extremely well the semantic of complex text sequences. Overall we removed only **@mentions**, **urls** and **emails** from every tweet and kept pretty much everything else.
+
+#### Hardware
+
+- **GPU**: Nvidia GTX1080ti
+- **CPU**: AMD Ryzen7 3700x 8c/16t
+- **RAM**: 64GB DDR4
+
+#### Hyperparameters
+
+- Optimizer: **AdamW** with learning rate of **2e-5**, epsilon of **1e-8**
+- Max epochs: **5**
+- Batch size: **32**
+- Early Stopping: **enabled** with patience = 1
+
+Early stopping was triggered after 3 epochs.
+  
+## Eval results 
+
+The model achieves an overall accuracy on the test set equal to 82%
+The test set is a 20% split of the whole dataset.
+
+## About us
+[Neuraly](https://neuraly.ai) is a young and dynamic startup committed to designing AI-driven solutions and services through the most advanced Machine Learning and Data Science technologies. You can find out more about who we are and what we do on our [website](https://neuraly.ai).
+
+## Acknowledgments
+
+Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
+it is possible to download the model from their S3 storage and live test it from their inference API 🤗.