Model save

README.md

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+---
+license: apache-2.0
+base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: para-multi-finetuning
+  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. -->
+
+# para-multi-finetuning
+
+This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7937
+- Accuracy: 0.8072
+- F1: 0.8054
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
+| 1.0969        | 1.0   | 65741  | 0.9992          | 0.7559   | 0.7478 |
+| 0.7092        | 2.0   | 131482 | 0.8674          | 0.7820   | 0.7787 |
+| 0.4966        | 3.0   | 197223 | 0.8120          | 0.7975   | 0.7927 |
+| 0.739         | 4.0   | 262964 | 0.7937          | 0.8072   | 0.8054 |
+| 0.4707        | 5.0   | 328705 | 0.8312          | 0.8109   | 0.8089 |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.4.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

all_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8072491215260348,
+    "eval_f1": 0.8053783422859715,
+    "eval_loss": 0.7936691641807556,
+    "eval_runtime": 460.123,
+    "eval_samples_per_second": 380.994,
+    "eval_steps_per_second": 47.624
+}

config.json

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+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+  "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": "auto_and_transport",
+    "1": "auto_and_transport/car_rental",
+    "2": "auto_and_transport/gas_and_fuel",
+    "3": "auto_and_transport/parking",
+    "4": "auto_and_transport/public_transportation",
+    "5": "auto_and_transport/service_and_parts",
+    "6": "auto_and_transport/taxi",
+    "7": "bills_and_utilities",
+    "8": "bills_and_utilities/internet",
+    "9": "bills_and_utilities/phone",
+    "10": "bills_and_utilities/television",
+    "11": "bills_and_utilities/utilities",
+    "12": "business_services",
+    "13": "business_services/accounting",
+    "14": "business_services/advertising",
+    "15": "business_services/office_supplies",
+    "16": "business_services/shipping",
+    "17": "education",
+    "18": "education/books_and_supplies",
+    "19": "education/student_loan",
+    "20": "education/tuition",
+    "21": "entertainment",
+    "22": "entertainment/amusement",
+    "23": "entertainment/arts",
+    "24": "entertainment/gambling",
+    "25": "entertainment/games",
+    "26": "entertainment/movies_and_music",
+    "27": "entertainment/newspapers_and_magazines",
+    "28": "fees_and_charges",
+    "29": "fees_and_charges/bankruptcy",
+    "30": "fees_and_charges/collections_agency",
+    "31": "fees_and_charges/loans",
+    "32": "fees_and_charges/loans/auto",
+    "33": "fees_and_charges/loans/high_cost",
+    "34": "fees_and_charges/loans/pawn",
+    "35": "fees_and_charges/loans/payday",
+    "36": "fees_and_charges/provider_fee",
+    "37": "fees_and_charges/provider_fee/penalty",
+    "38": "fees_and_charges/provider_fee/penalty/nsf",
+    "39": "fees_and_charges/provider_fee/penalty/overdraft",
+    "40": "fees_and_charges/provider_fee/penalty/stop_payment",
+    "41": "fees_and_charges/provider_fee/recurring",
+    "42": "fees_and_charges/service_fee",
+    "43": "fees_and_charges/service_fee/laundromat",
+    "44": "fees_and_charges/taxes",
+    "45": "fees_and_charges/union",
+    "46": "food_and_dining",
+    "47": "food_and_dining/alcohol_and_bars",
+    "48": "food_and_dining/cafes_and_restaurants",
+    "49": "food_and_dining/groceries",
+    "50": "food_and_dining/marijuana",
+    "51": "gifts_and_donations",
+    "52": "gifts_and_donations/charity",
+    "53": "gifts_and_donations/gifts",
+    "54": "health_and_fitness",
+    "55": "health_and_fitness/doctor",
+    "56": "health_and_fitness/personal_care",
+    "57": "health_and_fitness/pharmacy",
+    "58": "health_and_fitness/sports",
+    "59": "health_and_fitness/wellness",
+    "60": "home",
+    "61": "home/home_improvement",
+    "62": "home/home_services",
+    "63": "home/home_supplies",
+    "64": "home/mortgage",
+    "65": "home/mortgage_and_rent",
+    "66": "home/rent",
+    "67": "income",
+    "68": "income/bonus",
+    "69": "income/child_support",
+    "70": "income/government",
+    "71": "income/investment_income",
+    "72": "income/paycheck",
+    "73": "income/pension",
+    "74": "insurance",
+    "75": "insurance/car_insurance",
+    "76": "insurance/health_insurance",
+    "77": "insurance/life_insurance",
+    "78": "insurance/property_insurance",
+    "79": "kids",
+    "80": "kids/baby_supplies",
+    "81": "kids/babysitter_and_daycare",
+    "82": "kids/toys",
+    "83": "pets",
+    "84": "pets/pet_food_and_supplies",
+    "85": "pets/pet_grooming",
+    "86": "pets/veterinary",
+    "87": "shopping",
+    "88": "shopping/clothing",
+    "89": "shopping/electronics_and_software",
+    "90": "shopping/sporting_goods",
+    "91": "transfer",
+    "92": "transfer/atm",
+    "93": "transfer/credit_card",
+    "94": "transfer/email",
+    "95": "transfer/wire",
+    "96": "travel",
+    "97": "travel/hotel",
+    "98": "travel/transportation",
+    "99": "travel/vacation"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.43.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

eval_results.json

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+{
+    "epoch": 5.0,
+    "eval_accuracy": 0.8072491215260348,
+    "eval_f1": 0.8053783422859715,
+    "eval_loss": 0.7936691641807556,
+    "eval_runtime": 460.123,
+    "eval_samples_per_second": 380.994,
+    "eval_steps_per_second": 47.624
+}

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