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Initial README file

README.md

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+---
+language: es
+tags:
+- sagemaker
+- beto
+- TextClassification
+- SentimentAnalysis
+license: apache-2.0
+datasets:
+- IMDbreviews_es
+metrics:
+- accuracy
+model-index:
+- name: beto_sentiment_analysis_es
+  results:
+  - task:
+        name: Sentiment Analysis
+        type: sentiment-analysis
+    dataset:
+        name: "IMDb Reviews in Spanish" 
+        type: IMDbreviews_es
+    metrics:
+       - name: Accuracy,
+         type: accuracy,
+         value: 0.9101333333333333
+       - name: F1 Score,
+         type: f1,
+         value: 0.9088450094671354
+       - name: Precision,
+         type: precision,
+         value: 0.9105691056910569
+       - name: Recall,
+         type: recall,
+         value: 0.9071274298056156
+widget:
+- text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
+---
+
+# Model beto_sentiment_analysis_es
+
+## **A finetuned model for Sentiment analysis in Spanish**
+
+This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container,
+The base model is **BETO** which is a BERT-base model pre-trained  on a spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. 
+
+**BETO Citation**
+
+[Spanish Pre-Trained BERT Model and Evaluation Data](https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf)
+
+```
+@inproceedings{CaneteCFP2020,
+  title={Spanish Pre-Trained BERT Model and Evaluation Data},
+  author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
+  booktitle={PML4DC at ICLR 2020},
+  year={2020}
+}
+```
+
+## Dataset
+The dataset is a collection of movie reviews in Spanish, about 50,000 reviews. The dataset is balanced and provides every review in english, in spanish and the label in both languages. 
+
+Sizes of datasets:
+- Train dataset: 42,500
+- Validation dataset: 3,750
+- Test dataset: 3,750
+
+## Intended uses & limitations
+
+This model is intented for Sentiment Analysis for spanish corpus and finetuned specially for movie reviews but it can be applied to other kind of reviews.
+
+## Hyperparameters
+    {
+    "epochs": "4",
+    "train_batch_size": "32",    
+    "eval_batch_size": "8",
+    "fp16": "true",
+    "learning_rate": "3e-05",
+    "model_name": "\"dccuchile/bert-base-spanish-wwm-uncased\"",
+    "sagemaker_container_log_level": "20",
+    "sagemaker_program": "\"train.py\"",
+    }
+
+## Evaluation results
+
+- Accuracy = 0.9101333333333333
+
+- F1 Score = 0.9088450094671354
+
+- Precision = 0.9105691056910569
+
+- Recall = 0.9071274298056156
+
+## Test results
+
+## Model in action
+
+### Usage for Sentiment Analysis
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("edumunozsala/beto_sentiment_analysis_es")
+model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/beto_sentiment_analysis_es")
+
+text ="Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
+
+input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
+outputs = model(input_ids)
+output = outputs.logits.argmax(1)
+```
+
+Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala)