End of training

.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 +1,72 @@
+---
+library_name: transformers
+license: mit
+base_model: intfloat/multilingual-e5-large-instruct
+tags:
+- generated_from_trainer
+model-index:
+- name: e5_Eau_Multilabel_Topic_Sentiment
+  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. -->
+
+# e5_Eau_Multilabel_Topic_Sentiment
+
+This model is a fine-tuned version of [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3091
+- F1 Topic: 0.9267
+- F1 Sentiment: 0.9054
+- F1 Macro Avg: 0.9160
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 Topic | F1 Sentiment | F1 Macro Avg |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------:|:------------:|
+| 1.0405        | 1.0   | 98   | 0.5945          | 0.7469   | 0.8107       | 0.7788       |
+| 0.4667        | 2.0   | 196  | 0.2880          | 0.8879   | 0.8779       | 0.8829       |
+| 0.2356        | 3.0   | 294  | 0.2474          | 0.9179   | 0.8851       | 0.9015       |
+| 0.158         | 4.0   | 392  | 0.2425          | 0.9250   | 0.8873       | 0.9062       |
+| 0.1171        | 5.0   | 490  | 0.2999          | 0.9236   | 0.9083       | 0.9160       |
+| 0.0779        | 6.0   | 588  | 0.2869          | 0.9264   | 0.8896       | 0.9080       |
+| 0.0702        | 7.0   | 686  | 0.2964          | 0.9300   | 0.9060       | 0.9180       |
+| 0.071         | 8.0   | 784  | 0.3091          | 0.9267   | 0.9054       | 0.9160       |
+
+
+### Framework versions
+
+- Transformers 4.53.2
+- Pytorch 2.6.0+cu124
+- Datasets 4.0.0
+- Tokenizers 0.21.2

classifier.pt

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config.json

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+{
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "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": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_labels.json

@@ -0,0 +1 @@
+{"topic_labels": ["Eau - Gestion \u00e9conome de l'eau", "Eau - R\u00e9cup\u00e9ration de l'eau", "Eau - Sensibilisation aux usages de l'eau", "Eau - Traitement de l'eau", "Eau - \u00c9quitabilit\u00e9 des ressources"], "sentiment_labels": ["negative", "neutral", "positive"], "num_topic_labels": 5, "num_sentiment_labels": 3}

model.safetensors

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special_tokens_map.json

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tokenizer.json

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tokenizer_config.json

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+}

training_args.bin

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