Model save

README.md

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+---
+library_name: transformers
+license: mit
+base_model: intfloat/e5-small-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: e5-small-v2-sentiment-twitter
+  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-small-v2-sentiment-twitter
+
+This model is a fine-tuned version of [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6420
+- Accuracy: 0.7191
+- F1: 0.7184
+- Precision: 0.7232
+- Recall: 0.7191
+
+## 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: 16
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.7497        | 0.1754 | 500  | 0.7109          | 0.699    | 0.6941 | 0.7013    | 0.699  |
+| 0.6768        | 0.3508 | 1000 | 0.6320          | 0.7225   | 0.7229 | 0.7242    | 0.7225 |
+| 0.633         | 0.5261 | 1500 | 0.6572          | 0.706    | 0.7110 | 0.7340    | 0.706  |
+| 0.6566        | 0.7015 | 2000 | 0.6138          | 0.7235   | 0.7179 | 0.7311    | 0.7235 |
+| 0.6164        | 0.8769 | 2500 | 0.5928          | 0.754    | 0.7545 | 0.7589    | 0.754  |
+| 0.525         | 1.0523 | 3000 | 0.6018          | 0.75     | 0.7501 | 0.7510    | 0.75   |
+| 0.5706        | 1.2276 | 3500 | 0.5946          | 0.7525   | 0.7535 | 0.7554    | 0.7525 |
+| 0.5166        | 1.4030 | 4000 | 0.6254          | 0.753    | 0.7520 | 0.7540    | 0.753  |
+| 0.5242        | 1.5784 | 4500 | 0.5979          | 0.741    | 0.7423 | 0.7452    | 0.741  |
+| 0.4989        | 1.7538 | 5000 | 0.5992          | 0.754    | 0.7545 | 0.7560    | 0.754  |
+| 0.5139        | 1.9291 | 5500 | 0.5917          | 0.755    | 0.7552 | 0.7564    | 0.755  |
+
+
+### Framework versions
+
+- Transformers 4.55.4
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.4

all_results.json

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+{
+    "epoch": 2.0,
+    "eval_accuracy": 0.7191468577010746,
+    "eval_f1": 0.7183546184295306,
+    "eval_loss": 0.642044186592102,
+    "eval_precision": 0.7231504754230796,
+    "eval_recall": 0.7191468577010746,
+    "eval_runtime": 10.1399,
+    "eval_samples_per_second": 1211.452,
+    "eval_steps_per_second": 37.87
+}

model.safetensors

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-oid sha256:25f1070430a415ea516d1dcfb344d6499349e3e005ccbfb58ab32360ff93c90c
+oid sha256:b80b8f09b17c425c3103bdc16a7fc0173e07fa3553cd327775699bfbfa4e4bab
 size 133467916

test_results.json

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+{
+    "epoch": 2.0,
+    "eval_accuracy": 0.7191468577010746,
+    "eval_f1": 0.7183546184295306,
+    "eval_loss": 0.642044186592102,
+    "eval_precision": 0.7231504754230796,
+    "eval_recall": 0.7191468577010746,
+    "eval_runtime": 10.1399,
+    "eval_samples_per_second": 1211.452,
+    "eval_steps_per_second": 37.87
+}