Update config.json

config.json

@@ -1,36 +1,39 @@
----
-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"
----
+{
+  "_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,
+  "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
+}