Youseff1987
/

nllb-200-finetuning-20250305

@@ -1,199 +1,245 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+base_model: outputs/checkpoint-150
+tags:
+- generated_from_trainer
+model-index:
+- name: nllb-200-finetunning-5e-5-32batch-9310steps
+  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. -->
+# nllb-200-finetunning-5e-5-32batch-9310steps
+This model is a fine-tuned version of [outputs/checkpoint-150](https://huggingface.co/outputs/checkpoint-150) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9026
+## 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: 1
+- eval_batch_size: 1
+- seed: 3407
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 32
+- optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 5
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss | Model Preparation Time |
+|:-------------:|:------:|:----:|:---------------:|:----------------------:|
+| 1.3132        | 0.0268 | 50   | 1.0922          | 0.0202                 |
+| 1.3205        | 0.0537 | 100  | 1.0550          |
+| 1.1442        | 0.0805 | 150  | 1.0364          |
+| 0.9155        | 0.1074 | 200  | 1.0216          |
+| 1.2449        | 0.1342 | 250  | 1.0114          |
+| 0.7856        | 0.1611 | 300  | 1.0021          |
+| 1.1645        | 0.1879 | 350  | 0.9942          |
+| 1.168         | 0.2148 | 400  | 0.9872          |
+| 1.0991        | 0.2416 | 450  | 0.9800          |
+| 0.9243        | 0.2684 | 500  | 0.9751          |
+| 1.3314        | 0.2953 | 550  | 0.9689          |
+| 1.0203        | 0.3221 | 600  | 0.9650          |
+| 0.9849        | 0.3490 | 650  | 0.9630          |
+| 1.1588        | 0.3758 | 700  | 0.9581          |
+| 0.8723        | 0.4027 | 750  | 0.9560          |
+| 0.8312        | 0.4295 | 800  | 0.9528          |
+| 0.7363        | 0.4564 | 850  | 0.9519          |
+| 0.9292        | 0.4832 | 900  | 0.9494          |
+| 0.9326        | 0.5100 | 950  | 0.9449          |
+| 0.8994        | 0.5369 | 1000 | 0.9424          |
+| 1.0149        | 0.5637 | 1050 | 0.9401          |
+| 0.9467        | 0.5906 | 1100 | 0.9381          |
+| 1.0278        | 0.6174 | 1150 | 0.9375          |
+| 0.9975        | 0.6443 | 1200 | 0.9368          |
+| 1.0143        | 0.6711 | 1250 | 0.9342          |
+| 1.1696        | 0.6980 | 1300 | 0.9328          |
+| 0.8223        | 0.7248 | 1350 | 0.9316          |
+| 0.9384        | 0.7517 | 1400 | 0.9312          |
+| 0.9466        | 0.7785 | 1450 | 0.9296          |
+| 0.8791        | 0.8053 | 1500 | 0.9287          |
+| 1.1393        | 0.8322 | 1550 | 0.9287          |
+| 0.9288        | 0.8590 | 1600 | 0.9277          |
+| 0.9249        | 0.8859 | 1650 | 0.9260          |
+| 1.0053        | 0.9127 | 1700 | 0.9246          |
+| 0.7675        | 0.9396 | 1750 | 0.9235          |
+| 1.0448        | 0.9664 | 1800 | 0.9234          |
+| 0.8382        | 0.9933 | 1850 | 0.9214          |
+| 0.7418        | 1.0204 | 1900 | 0.9221          |
+| 0.8152        | 1.0472 | 1950 | 0.9234          |
+| 0.888         | 1.0741 | 2000 | 0.9225          |
+| 0.762         | 1.1009 | 2050 | 0.9213          |
+| 0.8621        | 1.1278 | 2100 | 0.9210          |
+| 0.9048        | 1.1546 | 2150 | 0.9200          |
+| 0.7952        | 1.1815 | 2200 | 0.9192          |
+| 0.9558        | 1.2083 | 2250 | 0.9186          |
+| 1.0422        | 1.2352 | 2300 | 0.9190          |
+| 0.7353        | 1.2620 | 2350 | 0.9167          |
+| 0.924         | 1.2888 | 2400 | 0.9169          |
+| 0.8895        | 1.3157 | 2450 | 0.9156          |
+| 0.9581        | 1.3425 | 2500 | 0.9149          |
+| 0.7616        | 1.3694 | 2550 | 0.9148          |
+| 0.77          | 1.3962 | 2600 | 0.9143          |
+| 0.8474        | 1.4231 | 2650 | 0.9134          |
+| 0.8242        | 1.4499 | 2700 | 0.9133          |
+| 0.8491        | 1.4768 | 2750 | 0.9131          |
+| 0.8286        | 1.5036 | 2800 | 0.9119          |
+| 0.7373        | 1.5305 | 2850 | 0.9116          |
+| 1.1709        | 1.5573 | 2900 | 0.9109          |
+| 0.918         | 1.5841 | 2950 | 0.9100          |
+| 0.8682        | 1.6110 | 3000 | 0.9104          |
+| 0.7289        | 1.6378 | 3050 | 0.9098          |
+| 0.9615        | 1.6647 | 3100 | 0.9098          |
+| 0.9054        | 1.6915 | 3150 | 0.9101          |
+| 0.9033        | 1.7184 | 3200 | 0.9094          |
+| 0.8673        | 1.7452 | 3250 | 0.9095          |
+| 1.0133        | 1.7721 | 3300 | 0.9078          |
+| 0.8208        | 1.7989 | 3350 | 0.9075          |
+| 0.8854        | 1.8257 | 3400 | 0.9072          |
+| 0.81          | 1.8526 | 3450 | 0.9074          |
+| 0.9013        | 1.8794 | 3500 | 0.9069          |
+| 0.8539        | 1.9063 | 3550 | 0.9064          |
+| 0.7346        | 1.9331 | 3600 | 0.9066          |
+| 0.9698        | 1.9600 | 3650 | 0.9061          |
+| 0.7256        | 1.9868 | 3700 | 0.9062          |
+| 0.8813        | 2.0134 | 3750 | 0.9057          |
+| 1.1117        | 2.0403 | 3800 | 0.9068          |
+| 0.766         | 2.0671 | 3850 | 0.9062          |
+| 0.8469        | 2.0940 | 3900 | 0.9066          |
+| 0.9628        | 2.1208 | 3950 | 0.9063          |
+| 0.9167        | 2.1476 | 4000 | 0.9062          |
+| 0.8287        | 2.1745 | 4050 | 0.9058          |
+| 0.866         | 2.2013 | 4100 | 0.9053          |
+| 0.9124        | 2.2282 | 4150 | 0.9055          |
+| 0.722         | 2.2550 | 4200 | 0.9057          |
+| 0.956         | 2.2819 | 4250 | 0.9056          |
+| 0.6837        | 2.3087 | 4300 | 0.9050          |
+| 1.0191        | 2.3356 | 4350 | 0.9045          |
+| 0.9707        | 2.3624 | 4400 | 0.9050          |
+| 0.9852        | 2.3892 | 4450 | 0.9054          |
+| 0.8172        | 2.4161 | 4500 | 0.9050          |
+| 0.979         | 2.4429 | 4550 | 0.9050          |
+| 0.9173        | 2.4698 | 4600 | 0.9042          |
+| 0.8936        | 2.4966 | 4650 | 0.9043          |
+| 0.6992        | 2.5235 | 4700 | 0.9045          |
+| 0.79          | 2.5503 | 4750 | 0.9045          |
+| 0.7661        | 2.5772 | 4800 | 0.9043          |
+| 0.9067        | 2.6040 | 4850 | 0.9036          |
+| 0.7251        | 2.6309 | 4900 | 0.9035          |
+| 0.7873        | 2.6577 | 4950 | 0.9036          |
+| 0.8441        | 2.6845 | 5000 | 0.9034          |
+| 0.9242        | 2.7114 | 5050 | 0.9034          |
+| 0.8931        | 2.7382 | 5100 | 0.9029          |
+| 1.0847        | 2.7651 | 5150 | 0.9028          |
+| 0.7797        | 2.7919 | 5200 | 0.9028          |
+| 0.7537        | 2.8188 | 5250 | 0.9030          |
+| 0.7131        | 2.8456 | 5300 | 0.9030          |
+| 0.8321        | 2.8725 | 5350 | 0.9030          |
+| 0.7554        | 2.8993 | 5400 | 0.9032          |
+| 0.8003        | 2.9261 | 5450 | 0.9032          |
+| 0.862         | 2.9530 | 5500 | 0.9034          |
+| 0.9439        | 2.9798 | 5550 | 0.9031          |
+| 0.7934        | 3.0064 | 5600 | 0.9030          |
+| 0.7656        | 3.0333 | 5650 | 0.9030          |
+| 1.0536        | 3.0601 | 5700 | 0.9033          |
+| 0.7046        | 3.0870 | 5750 | 0.9032          |
+| 0.7297        | 3.1138 | 5800 | 0.9028          |
+| 0.7948        | 3.1407 | 5850 | 0.9028          |
+| 0.7877        | 3.1675 | 5900 | 0.9030          |
+| 0.8918        | 3.1944 | 5950 | 0.9028          |
+| 0.8123        | 3.2212 | 6000 | 0.9030          |
+| 0.7079        | 3.2480 | 6050 | 0.9029          |
+| 0.9428        | 3.2749 | 6100 | 0.9030          |
+| 0.7774        | 3.3017 | 6150 | 0.9030          |
+| 0.8418        | 3.3286 | 6200 | 0.9033          |
+| 1.0364        | 3.3554 | 6250 | 0.9032          |
+| 0.7611        | 3.3823 | 6300 | 0.9031          |
+| 0.8938        | 3.4091 | 6350 | 0.9030          |
+| 0.9085        | 3.4360 | 6400 | 0.9030          |
+| 0.8015        | 3.4628 | 6450 | 0.9030          |
+| 0.7286        | 3.4896 | 6500 | 0.9030          |
+| 0.7203        | 3.5165 | 6550 | 0.9030          |
+| 0.8212        | 3.5433 | 6600 | 0.9030          |
+| 0.7335        | 3.5702 | 6650 | 0.9028          |
+| 0.7196        | 3.5970 | 6700 | 0.9029          |
+| 0.6572        | 3.6239 | 6750 | 0.9030          |
+| 0.8649        | 3.6507 | 6800 | 0.9029          |
+| 0.805         | 3.6776 | 6850 | 0.9029          |
+| 0.8108        | 3.7044 | 6900 | 0.9027          |
+| 0.8756        | 3.7313 | 6950 | 0.9028          |
+| 0.895         | 3.7581 | 7000 | 0.9026          |
+| 0.8497        | 3.7849 | 7050 | 0.9028          |
+| 0.9445        | 3.8118 | 7100 | 0.9026          |
+| 0.7153        | 3.8386 | 7150 | 0.9026          |
+| 0.7897        | 3.8655 | 7200 | 0.9026          |
+| 0.858         | 3.8923 | 7250 | 0.9027          |
+| 0.9963        | 3.9192 | 7300 | 0.9028          |
+| 0.7619        | 3.9460 | 7350 | 0.9027          |
+| 0.8844        | 3.9729 | 7400 | 0.9028          |
+| 0.8264        | 3.9997 | 7450 | 0.9028          |
+| 0.9657        | 4.0263 | 7500 | 0.9026          |
+| 0.7688        | 4.0532 | 7550 | 0.9028          |
+| 0.9613        | 4.0800 | 7600 | 0.9027          |
+| 0.7074        | 4.1068 | 7650 | 0.9025          |
+| 0.7589        | 4.1337 | 7700 | 0.9028          |
+| 0.8279        | 4.1605 | 7750 | 0.9028          |
+| 0.7417        | 4.1874 | 7800 | 0.9027          |
+| 0.8121        | 4.2142 | 7850 | 0.9026          |
+| 0.877         | 4.2411 | 7900 | 0.9026          |
+| 0.7371        | 4.2679 | 7950 | 0.9027          |
+| 0.8387        | 4.2948 | 8000 | 0.9027          |
+| 0.8789        | 4.3216 | 8050 | 0.9028          |
+| 1.0297        | 4.3484 | 8100 | 0.9027          |
+| 0.7222        | 4.3753 | 8150 | 0.9028          |
+| 0.8673        | 4.4021 | 8200 | 0.9027          |
+| 0.7866        | 4.4290 | 8250 | 0.9027          |
+| 0.7187        | 4.4558 | 8300 | 0.9027          |
+| 0.8237        | 4.4827 | 8350 | 0.9027          |
+| 0.8223        | 4.5095 | 8400 | 0.9027          |
+| 0.8093        | 4.5364 | 8450 | 0.9027          |
+| 0.815         | 4.5632 | 8500 | 0.9026          |
+| 0.7278        | 4.5900 | 8550 | 0.9028          |
+| 0.7515        | 4.6169 | 8600 | 0.9027          |
+| 0.9041        | 4.6437 | 8650 | 0.9026          |
+| 0.7683        | 4.6706 | 8700 | 0.9026          |
+| 0.8538        | 4.6974 | 8750 | 0.9027          |
+| 0.837         | 4.7243 | 8800 | 0.9027          |
+| 0.7077        | 4.7511 | 8850 | 0.9027          |
+| 0.8734        | 4.7780 | 8900 | 0.9027          |
+| 0.8391        | 4.8048 | 8950 | 0.9027          |
+| 0.7243        | 4.8316 | 9000 | 0.9028          |
+| 0.6905        | 4.8585 | 9050 | 0.9026          |
+| 0.8787        | 4.8853 | 9100 | 0.9026          |
+| 0.9105        | 4.9122 | 9150 | 0.9026          |
+| 0.9295        | 4.9390 | 9200 | 0.9025          |
+| 1.0437        | 4.9659 | 9250 | 0.9026          |
+| 0.9296        | 4.9927 | 9300 | 0.9026          |
+### Framework versions
+- Transformers 4.49.0
+- Pytorch 2.1.0+cu118
+- Datasets 3.3.2
+- Tokenizers 0.21.0

training_args.bin ADDED Viewed

	@@ -0,0 +1,3 @@

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