Instructions to use mgh6/TCS_MLM_All with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/TCS_MLM_All with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/TCS_MLM_All")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/TCS_MLM_All") model = AutoModelForMaskedLM.from_pretrained("mgh6/TCS_MLM_All") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 900, checkpoint
Browse files- last-checkpoint/model.safetensors +1 -1
- last-checkpoint/optimizer.pt +1 -1
- last-checkpoint/rng_state_0.pth +1 -1
- last-checkpoint/rng_state_1.pth +1 -1
- last-checkpoint/rng_state_2.pth +1 -1
- last-checkpoint/rng_state_3.pth +1 -1
- last-checkpoint/scheduler.pt +1 -1
- last-checkpoint/trainer_state.json +19 -4
last-checkpoint/model.safetensors
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last-checkpoint/rng_state_0.pth
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last-checkpoint/scheduler.pt
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last-checkpoint/trainer_state.json
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{
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"logging_steps": 100,
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"TrainerControl": {
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{
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"eval_samples_per_second": 85.156,
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"eval_steps_per_second": 4.482,
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"step": 800
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"eval_steps_per_second": 4.577,
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"step": 900
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"logging_steps": 100,
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"early_stopping_threshold": 0.0
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"attributes": {
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"TrainerControl": {
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"attributes": {}
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