In that case what is the correspondance with the epochs? Secondly I wonder how to change the frequency of training losses points in TensorBoard plots. In the custom file I change the rec_results_freq, but for me it has only an impact on the validation and inference plots. The training losses are always plotted every...
如图7 所示,强化微调(ReFT)过程分两个阶段执行。上半部分表示有监督微调(SFT)阶段,在这个阶段中,模型在多个训练轮次(epochs)里对训练数据进行迭代,以学习每个问题的正确思维链(CoT)注释。下半部分引入了强化微调阶段:从经过有监督微调训练的模型开始,该模型基于其当前策略生成替代的思维链注释,并将其预测答案与真实...
翻译数据:我们试图避免使用机器翻译数据来微调模型,以防止翻译体(Bizzoni 等人,2020 年;Muennighoff 等人,2023 年)或可能出现的名称偏差(Wang 等人,2022a)、性别偏差(Savoldi 等人,2021 年)或文化偏见(Ji 等人,2023 年)。 此外,我们的目标是防止模型仅暴露于植根于英语文化背景的任务中,这可能不能代表我们想要捕...
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We compute a standard classification loss with CC and WW, i.e., log(softmax(CWT))log(softmax(CWT)). 我们使用32的batch_size,并对所有GLUE任务的数据进行3个epochs的微调。对于每个任务,我们在开发集上选择最佳微调学习率(5e-5、4e-5、3e-5和2e-5)。此外,对于BERTLARGEBERTLARGE,我们发现在小数据...
there is a long tail. Some filters are important and contribute over 4% of accuracy but most filters are around 1%. This implies that even a tiny and under-performing network could be filter pruned without significant performance loss. The model has not efficiently allocated fi...
N=3. All models were trained for 150 epochs with similar learning rate schedule and initialization. DSD and RePr (Weights) perform roughly the same function - sparsifying the model guided by magnitude, with the difference that DSD acts on individual weights, while RePr (Weights) acts on entire...
2) Workload: We evaluate AntDT by three typical workloads over two open-source benchmarks and one Ant Group production dataset in the TensorFlow Parameter Server and PyTorch DDP/AllReduce strategy. Firstly, we train the XDeepFM [36] on three epochs of public Criteo dataset [57] (containing...
Tensorboard can be used to compare train and eval metrics over epochs, see the Tensorflow model graph, and much more. The accuracy metric most commonly used for this task is the BLEU score. This model achieved a BLEU score to around 28 for French to English translation, which is considered...
Sudden drop in loss while training a model and stuck at the same loss and accuracy for the last 5 epochs