multi-e5-small_lmd-comments_v1

README.md

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+---
+license: mit
+base_model: intfloat/multilingual-e5-small
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- accuracy
+model-index:
+- name: multi-e5-small_lmd-comments_v1
+  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. -->
+
+# multi-e5-small_lmd-comments_v1
+
+This model is a fine-tuned version of [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9808
+- F1: 0.7036
+- Accuracy: 0.7122
+
+## 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
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
+| 1.0969        | 0.04  | 100  | 1.0991          | 0.4109 | 0.4964   |
+| 1.0764        | 0.08  | 200  | 1.0768          | 0.5217 | 0.5971   |
+| 0.955         | 0.12  | 300  | 0.9313          | 0.5802 | 0.6691   |
+| 0.8137        | 0.17  | 400  | 0.8927          | 0.5864 | 0.6475   |
+| 0.7837        | 0.21  | 500  | 0.8711          | 0.6238 | 0.6475   |
+| 0.7234        | 0.25  | 600  | 0.9953          | 0.5641 | 0.6475   |
+| 0.6983        | 0.29  | 700  | 0.9111          | 0.6226 | 0.6475   |
+| 0.6574        | 0.33  | 800  | 0.8557          | 0.6686 | 0.6835   |
+| 0.6653        | 0.37  | 900  | 0.7925          | 0.7087 | 0.7122   |
+| 0.6444        | 0.41  | 1000 | 0.8338          | 0.7056 | 0.7122   |
+| 0.6155        | 0.46  | 1100 | 0.8339          | 0.7257 | 0.7338   |
+| 0.5726        | 0.5   | 1200 | 0.8078          | 0.7140 | 0.7194   |
+| 0.6279        | 0.54  | 1300 | 0.9534          | 0.6917 | 0.7050   |
+| 0.6083        | 0.58  | 1400 | 0.9515          | 0.6914 | 0.7050   |
+| 0.5525        | 0.62  | 1500 | 0.9281          | 0.6846 | 0.7050   |
+| 0.6849        | 0.66  | 1600 | 0.8352          | 0.6917 | 0.7050   |
+| 0.5924        | 0.7   | 1700 | 1.0702          | 0.6602 | 0.6906   |
+| 0.5614        | 0.75  | 1800 | 0.9689          | 0.6801 | 0.6978   |
+| 0.5936        | 0.79  | 1900 | 1.0179          | 0.6896 | 0.7050   |
+| 0.5582        | 0.83  | 2000 | 0.8858          | 0.7320 | 0.7410   |
+| 0.5479        | 0.87  | 2100 | 0.9373          | 0.7030 | 0.7122   |
+| 0.6278        | 0.91  | 2200 | 0.8694          | 0.6858 | 0.6978   |
+| 0.4819        | 0.95  | 2300 | 0.9440          | 0.7074 | 0.7194   |
+| 0.5425        | 0.99  | 2400 | 1.0661          | 0.6765 | 0.6906   |
+| 0.5804        | 1.04  | 2500 | 0.8904          | 0.7189 | 0.7266   |
+| 0.5025        | 1.08  | 2600 | 1.0105          | 0.6886 | 0.7050   |
+| 0.5148        | 1.12  | 2700 | 0.9934          | 0.7076 | 0.7194   |
+| 0.5359        | 1.16  | 2800 | 0.9249          | 0.7291 | 0.7410   |
+| 0.5002        | 1.2   | 2900 | 0.7503          | 0.7047 | 0.7050   |
+| 0.4563        | 1.24  | 3000 | 0.8149          | 0.7230 | 0.7266   |
+| 0.4837        | 1.28  | 3100 | 0.8956          | 0.7125 | 0.7194   |
+| 0.4486        | 1.33  | 3200 | 0.9013          | 0.7110 | 0.7194   |
+| 0.4721        | 1.37  | 3300 | 1.0545          | 0.7142 | 0.7266   |
+| 0.5482        | 1.41  | 3400 | 1.0139          | 0.7014 | 0.7122   |
+| 0.4488        | 1.45  | 3500 | 0.9427          | 0.7162 | 0.7266   |
+| 0.4859        | 1.49  | 3600 | 1.1337          | 0.7074 | 0.7194   |
+| 0.504         | 1.53  | 3700 | 1.0299          | 0.7178 | 0.7266   |
+| 0.4555        | 1.57  | 3800 | 0.8830          | 0.7273 | 0.7338   |
+| 0.502         | 1.62  | 3900 | 1.0340          | 0.7142 | 0.7266   |
+| 0.5131        | 1.66  | 4000 | 1.0997          | 0.7031 | 0.7194   |
+| 0.5208        | 1.7   | 4100 | 1.0845          | 0.7025 | 0.7194   |
+| 0.4329        | 1.74  | 4200 | 1.0553          | 0.7132 | 0.7266   |
+| 0.4612        | 1.78  | 4300 | 1.0458          | 0.7074 | 0.7194   |
+| 0.4857        | 1.82  | 4400 | 0.9425          | 0.7120 | 0.7194   |
+| 0.4986        | 1.86  | 4500 | 0.9965          | 0.7237 | 0.7338   |
+| 0.4066        | 1.91  | 4600 | 0.9520          | 0.7041 | 0.7122   |
+| 0.4638        | 1.95  | 4700 | 0.9558          | 0.6979 | 0.7050   |
+| 0.4541        | 1.99  | 4800 | 0.9808          | 0.7036 | 0.7122   |
+
+
+### Framework versions
+
+- Transformers 4.38.1
+- Pytorch 2.1.2
+- Datasets 2.1.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "intfloat/multilingual-e5-small",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

model.safetensors

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+size 470643204

training_args.bin

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