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wandb: Currently logged in as: priyanshi-pal (priyanshipal). Use `wandb login --relogin` to force relogin |
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wandb: wandb version 0.17.7 is available! To upgrade, please run: |
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wandb: $ pip install wandb --upgrade |
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wandb: Tracking run with wandb version 0.17.6 |
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wandb: Run data is saved locally in /scratch/elec/t405-puhe/p/palp3/MUCS/wandb/run-20240822_150154-1kodfy70 |
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wandb: Run `wandb offline` to turn off syncing. |
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wandb: Syncing run eval_pd2000_s300_shuff100_hindi |
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wandb: ⭐️ View project at https://wandb.ai/priyanshipal/huggingface |
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wandb: 🚀 View run at https://wandb.ai/priyanshipal/huggingface/runs/1kodfy70 |
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/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead |
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warnings.warn( |
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/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py:957: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead. |
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warnings.warn( |
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/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/auto/feature_extraction_auto.py:329: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead. |
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warnings.warn( |
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/scratch/work/palp3/myenv/lib/python3.11/site-packages/accelerate/accelerator.py:488: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. |
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self.scaler = torch.cuda.amp.GradScaler(**kwargs) |
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max_steps is given, it will override any value given in num_train_epochs |
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Wav2Vec2CTCTokenizer(name_or_path='', vocab_size=149, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '[UNK]', 'pad_token': '[PAD]'}, clean_up_tokenization_spaces=True), added_tokens_decoder={ |
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147: AddedToken("[UNK]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False), |
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148: AddedToken("[PAD]", rstrip=True, lstrip=True, single_word=False, normalized=False, special=False), |
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149: AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), |
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150: AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), |
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} |
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CHECK MODEL PARAMS Wav2Vec2ForCTC( |
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(wav2vec2): Wav2Vec2Model( |
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(feature_extractor): Wav2Vec2FeatureEncoder( |
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(conv_layers): ModuleList( |
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(0): Wav2Vec2LayerNormConvLayer( |
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(conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,)) |
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(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(activation): GELUActivation() |
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) |
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(1-4): 4 x Wav2Vec2LayerNormConvLayer( |
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(conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,)) |
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(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(activation): GELUActivation() |
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) |
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(5-6): 2 x Wav2Vec2LayerNormConvLayer( |
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(conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,)) |
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(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(activation): GELUActivation() |
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) |
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) |
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) |
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(feature_projection): Wav2Vec2FeatureProjection( |
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(layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) |
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(projection): Linear(in_features=512, out_features=1024, bias=True) |
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(dropout): Dropout(p=0.0, inplace=False) |
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) |
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(encoder): Wav2Vec2EncoderStableLayerNorm( |
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(pos_conv_embed): Wav2Vec2PositionalConvEmbedding( |
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(conv): ParametrizedConv1d( |
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1024, 1024, kernel_size=(128,), stride=(1,), padding=(64,), groups=16 |
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(parametrizations): ModuleDict( |
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(weight): ParametrizationList( |
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(0): _WeightNorm() |
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) |
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) |
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) |
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(padding): Wav2Vec2SamePadLayer() |
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(activation): GELUActivation() |
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) |
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(layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.0, inplace=False) |
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(layers): ModuleList( |
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(0-23): 24 x Wav2Vec2EncoderLayerStableLayerNorm( |
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(attention): Wav2Vec2SdpaAttention( |
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(k_proj): Linear(in_features=1024, out_features=1024, bias=True) |
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(v_proj): Linear(in_features=1024, out_features=1024, bias=True) |
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(q_proj): Linear(in_features=1024, out_features=1024, bias=True) |
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(out_proj): Linear(in_features=1024, out_features=1024, bias=True) |
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) |
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(dropout): Dropout(p=0.0, inplace=False) |
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(layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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(feed_forward): Wav2Vec2FeedForward( |
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(intermediate_dropout): Dropout(p=0.0, inplace=False) |
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(intermediate_dense): Linear(in_features=1024, out_features=4096, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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(output_dense): Linear(in_features=4096, out_features=1024, bias=True) |
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(output_dropout): Dropout(p=0.0, inplace=False) |
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) |
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(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
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) |
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) |
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) |
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) |
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(dropout): Dropout(p=0.0, inplace=False) |
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(lm_head): Linear(in_features=1024, out_features=151, bias=True) |
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) |
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08/22/2024 15:02:06 - INFO - __main__ - *** Evaluate *** |
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/scratch/work/palp3/myenv/lib/python3.11/site-packages/transformers/models/wav2vec2/processing_wav2vec2.py:157: UserWarning: `as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your labels by using the argument `text` of the regular `__call__` method (either in the same call as your audio inputs, or in a separate call. |
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warnings.warn( |
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Printing predictions for a few samples: |
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Sample 1: |
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Reference: हम उनका उपयोग ऐसे ही कर सकते हैं या आवश्यकता अनुसार कुछ बदलाव करके उपयोग कर सकते हैं |
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Prediction: |
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Sample 2: |
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Reference: अतः शीर्षक इस तरह से जोड़ सकते हैं |
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Prediction: |
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Sample 3: |
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Reference: प्रेसेंटेशन के अंत में आपने स्लाइड की एक कॉपी बना ली है |
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Prediction: |
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Sample 4: |
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Reference: चलिए अब फोंट्स और फोंट्स को फॉर्मेट करने के कुछ तरीके देखते हैं |
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Prediction: |
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Sample 5: |
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Reference: यह एक डायलॉग बॉक्स खोलेगा जिसमें हम अपनी आवश्यकतानुसार फॉन्ट स्टाइल और साइज़ सेट कर सकते हैं |
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Prediction: |
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last Reference string यह स्क्रिप्ट लता द्वारा अनुवादित है आईआईटी मुंबई की ओर से मैं रवि कुमार अब आपसे विदा लेता हूँहमसे जुड़ने के लिए धन्यवाद |
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last prediction string |
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***** eval metrics ***** |
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eval_cer = 1.0 |
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eval_loss = nan |
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eval_model_preparation_time = 0.0046 |
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eval_runtime = 0:00:39.88 |
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eval_samples = 572 |
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eval_samples_per_second = 14.34 |
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eval_steps_per_second = 0.902 |
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eval_wer = 1.0 |
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training_args.bin: 0%| | 0.00/5.43k [00:00<?, ?B/s]
training_args.bin: 100%|██████████| 5.43k/5.43k [00:00<00:00, 28.4kB/s] |
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wandb: - 0.005 MB of 0.005 MB uploaded
wandb: \ 0.036 MB of 0.036 MB uploaded
wandb: |
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wandb: Run history: |
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wandb: eval/cer ▁ |
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wandb: eval/model_preparation_time ▁ |
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wandb: eval/runtime ▁ |
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wandb: eval/samples_per_second ▁ |
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wandb: eval/steps_per_second ▁ |
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wandb: eval/wer ▁ |
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wandb: eval_cer ▁ |
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wandb: eval_model_preparation_time ▁ |
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wandb: eval_runtime ▁ |
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wandb: eval_samples ▁ |
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wandb: eval_samples_per_second ▁ |
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wandb: eval_steps_per_second ▁ |
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wandb: eval_wer ▁ |
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wandb: train/global_step ▁▁ |
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wandb: |
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wandb: Run summary: |
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wandb: eval/cer 1.0 |
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wandb: eval/loss nan |
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wandb: eval/model_preparation_time 0.0046 |
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wandb: eval/runtime 39.8895 |
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wandb: eval/samples_per_second 14.34 |
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wandb: eval/steps_per_second 0.902 |
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wandb: eval/wer 1.0 |
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wandb: eval_cer 1.0 |
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wandb: eval_loss nan |
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wandb: eval_model_preparation_time 0.0046 |
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wandb: eval_runtime 39.8895 |
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wandb: eval_samples 572 |
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wandb: eval_samples_per_second 14.34 |
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wandb: eval_steps_per_second 0.902 |
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wandb: eval_wer 1.0 |
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wandb: train/global_step 0 |
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wandb: |
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wandb: 🚀 View run eval_pd2000_s300_shuff100_hindi at: https://wandb.ai/priyanshipal/huggingface/runs/1kodfy70 |
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wandb: ⭐️ View project at: https://wandb.ai/priyanshipal/huggingface |
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wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) |
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wandb: Find logs at: ./wandb/run-20240822_150154-1kodfy70/logs |
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wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information. |
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