Update config.json

config.json

@@ -1,40 +1,36 @@
-{
-  "_name_or_path": "cardiffnlp/twitter-xlm-roberta-base-sentiment",
-  "architectures": [
-    "XLMRobertaForSequenceClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "classifier_dropout": null,
-  "eos_token_id": 2,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "negative",
-    "1": "neutral",
-    "2": "positive"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "label2id": {
-    "negative": 0,
-    "neutral": 1,
-    "positive": 2
-  },
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 514,
-  "model_type": "xlm-roberta",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "output_past": true,
-  "pad_token_id": 1,
-  "position_embedding_type": "absolute",
-  "problem_type": "single_label_classification",
-  "torch_dtype": "float32",
-  "transformers_version": "4.41.2",
-  "type_vocab_size": 1,
-  "use_cache": true,
-  "vocab_size": 250002
-}
+---
+language: es
+tags:
+•⁠  ⁠sentiment-analysis
+•⁠  ⁠text-classification
+•⁠  ⁠spanish
+•⁠  ⁠xlm-roberta
+datasets:
+•⁠  ⁠custom  # Reemplaza esto con el nombre del dataset si es público
+metrics:
+•⁠  ⁠accuracy
+•⁠  ⁠f1
+license: mit  # Ajusta esto según la licencia de tu modelo
+model-index:
+•⁠  ⁠name: bert-bregman
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Analysis
+    dataset:
+      name: Custom Spanish Sentiment Dataset
+      type: custom  # Ajusta esto si usaste un dataset público
+    metrics:
+      - type: accuracy
+        value: 0.7432432432432432
+      - type: f1
+        value: 0.7330748170322471
+pipeline_tag: text-classification
+widget:
+  - text: "Me encanta este producto, es excelente!"
+    example_title: "Ejemplo positivo"
+  - text: "No estoy seguro si me gusta o no."
+    example_title: "Ejemplo neutro"
+  - text: "Este servicio es terrible, nunca lo recomendaría."
+    example_title: "Ejemplo negativo"
+---