Model save

README.md

@@ -1,54 +1,53 @@
 ---
-datasets:
-- albertmartinez/OSDG
-license: mit
-metrics:
-- accuracy
-- precision
-- recall
-- f1
-pipeline_tag: text-classification
-widget:
-- text: Between the Social and the Spatial - Exploring Multiple Dimensions of Poverty
-    and Social Exclusion, Ashgate. Poverty in Europe and the USA, Exchanging Official
-    Measurement Methods”, Maastricht Graduate School of Governance Working Paper 2007/005.
-    Monitoring Absolute and Relative Poverty, ‘Not Enough’ Is Not the Same as ‘Much
-    Less’”, Review of Income and Wealth, 57(2), 247-269. Poverty and Social Exclusion
-    in Britain, The Policy Press, Bristol.
-- text: A circular economy is a way of achieving sustainable consumption and production,
-    as well as nature positive outcomes.
+license: apache-2.0
+base_model: google-bert/bert-base-multilingual-uncased
+tags:
+- generated_from_trainer
 model-index:
-- name: albertmartinez/bert-multilingual-sdg-classification
-  results:
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: albertmartinez/OSDG
-      type: albertmartinez/OSDG
-      split: test
-    metrics:
-    - type: accuracy
-      value: 0.8063482135876788
-      name: accuracy
+- name: bert-multilingual-sdg-classification
+  results: []
 ---
 
-# albertmartinez/bert-multilingual-sdg-classification
+<!-- 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. -->
 
-This model (BERT) is for classifying text with respect to the United Nations sustainable development goals (SDG).
+# bert-multilingual-sdg-classification
 
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on an unknown dataset.
+
+## 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: 32
+- 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: 600
+- num_epochs: 3.0
 
 ### Training results
 
-| epoch | eval_loss          | eval_accuracy      | eval_precision     | eval_recall        | eval_f1            |
-|:-----:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
-| 1     | 0.932697594165802  | 0.7336408412164803 | 0.7360917875267202 | 0.7336408412164803 | 0.7251914694909984 |
-| 2     | 0.7605898976325989 | 0.777343444609491  | 0.7831176862515112 | 0.777343444609491  | 0.777992037469195  |
-| 3     | 0.7255606651306152 | 0.7918849190837307 | 0.7922937492519824 | 0.7918849190837307 | 0.7897436793752113 |
-| 4     | 0.7620322108268738 | 0.782581502619029  | 0.791463371337951  | 0.782581502619029  | 0.7836557631495363 |
-| 5     | 0.7925569415092468 | 0.7989211164099758 | 0.7987793726148532 | 0.7989211164099758 | 0.7976319242111907 |
-| 6     | 0.8901194930076599 | 0.8008756156672661 | 0.8026922096228485 | 0.8008756156672661 | 0.7999251604304751 |
-| 7     | 0.9644309878349304 | 0.797044797122977  | 0.7998531076533889 | 0.797044797122977  | 0.7979133030034837 |
-| 8     | 1.0754749774932861 | 0.8050973340630131 | 0.8031314135784893 | 0.8050973340630131 | 0.8026788633046699 |
-| 9     | 1.1106163263320923 | 0.8066609334688453 | 0.806263013139723  | 0.8066609334688453 | 0.8061921653404509 |
-| 10    | 1.1396784782409668 | 0.8066609334688453 | 0.8056981354865757 | 0.8066609334688453 | 0.8059943909432397 |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1