Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- dair-ai/emotion
+language:
+- en
+metrics:
+- accuracy
+tags:
+- emotion
+---
+# Model
+
+Model IA Berta_Base_Uncased entrened with dateset emotion
+
+## Model Details
+
+Model Base: bert_base_uncased
+
+dataset: dair-ai/emotion
+
+Config train:
+
+num_train_epochs= 8
+learning_rate= 2e-5
+weight_decay=0.01
+batch_size: 64
+
+## Eval Exam
+```json
+{
+ 'test_loss': 0.14830373227596283
+ 'test_accuracy': 0.9415
+ 'test_f1': 0.9411005763302622
+ 'test_runtime': 8.372
+ 'test_samples_per_second': 238.892
+ 'test_steps_per_second': 3.822
+  }
+```
+
+ ## How to Use the model:
+```python
+from transformers import pipeline
+model_path = "daveni/twitter-xlm-roberta-emotion-es"
+emotion_analysis = pipeline("text-classification", framework="pt", model=model_path, tokenizer=model_path)
+emotion_analysis("Einstein dijo: Solo hay dos cosas infinitas, el universo y los pinches anuncios de bitcoin en Twitter. Paren ya carajo aaaaaaghhgggghhh me quiero murir")
+```
+```
+[{'label': 'anger', 'score': 0.48307016491889954}]
+```
+## Full classification example
+```python
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+# Preprocess text (username and link placeholders)
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+model_path = "Cesar42/bert-base-uncased-emotion_v2"
+tokenizer = AutoTokenizer.from_pretrained(model_path )
+config = AutoConfig.from_pretrained(model_path )
+# PT
+model = AutoModelForSequenceClassification.from_pretrained(model_path )
+text = "Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal."
+text = preprocess(text)
+print(text)
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+scores = output[0][0].detach().numpy()
+scores = softmax(scores)
+# Print labels and scores
+ranking = np.argsort(scores)
+ranking = ranking[::-1]
+for i in range(scores.shape[0]):
+    l = config.id2label[ranking[i]]
+    s = scores[ranking[i]]
+    print(f"{i+1}) {l} {np.round(float(s), 4)}")
+```
+Output: 
+
+```
+Se ha quedao bonito día para publicar vídeo, ¿no? Hoy del tema más diferente que hemos tocado en el canal.
+1) joy 0.7887
+2) others 0.1679
+3) surprise 0.0152
+4) sadness 0.0145
+5) anger 0.0077
+6) disgust 0.0033
+7) fear 0.0027
+```
+
+### Referece
+
+* bhadresh-savani/bert-base-uncased-emotion
+* [Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb). bhadresh-savani
+