louisbrulenaudet/lemone-router-m

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,66 @@
+---
+library_name: transformers
+license: mit
+base_model: intfloat/multilingual-e5-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: lemone-router
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# lemone-router
+
+This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4096
+- Accuracy: 0.9265
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 4.099463734610582e-05
+- train_batch_size: 16
+- eval_batch_size: 64
+- seed: 23
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.5371        | 1.0   | 2809  | 0.4147          | 0.8680   |
+| 0.3154        | 2.0   | 5618  | 0.3470          | 0.8914   |
+| 0.2241        | 3.0   | 8427  | 0.3345          | 0.9147   |
+| 0.1273        | 4.0   | 11236 | 0.3788          | 0.9187   |
+| 0.0525        | 5.0   | 14045 | 0.4096          | 0.9265   |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.4.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.20.1

config.json

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+{
+  "_name_or_path": "intfloat/multilingual-e5-base",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B\u00e9n\u00e9fices professionnels",
+    "1": "Contr\u00f4le et contentieux",
+    "2": "Dispositifs transversaux",
+    "3": "Fiscalit\u00e9 des entreprises",
+    "4": "Patrimoine et enregistrement",
+    "5": "Revenus particuliers",
+    "6": "Revenus patrimoniaux",
+    "7": "Taxes sur la consommation"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B\u00e9n\u00e9fices professionnels": 0,
+    "Contr\u00f4le et contentieux": 1,
+    "Dispositifs transversaux": 2,
+    "Fiscalit\u00e9 des entreprises": 3,
+    "Patrimoine et enregistrement": 4,
+    "Revenus particuliers": 5,
+    "Revenus patrimoniaux": 6,
+    "Taxes sur la consommation": 7
+  },
+  "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": "4.45.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

emissions.csv

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special_tokens_map.json

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tokenizer_config.json

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