wilsonmarciliojr
/

crammed-bert-portuguese

@@ -1,199 +1,143 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- bert
+- cramming
+- NLU
+license: apache-2.0
+datasets:
+- TucanoBR/GigaVerbo
+language:
+- pt
+pipeline_tag: fill-mask
 ---
+# crammed BERT Portuguese
 <!-- Provide a quick summary of what the model is/does. -->
+This is a BERT model trained for 24 hours on a single A6000 GPU. It follows the architecture described in "Cramming: Training a Language Model on a Single GPU in One Day".
+To use this model, clone the code from my fork https://github.com/wilsonjr/cramming and `import cramming` before using the 🤗 transformers `AutoModel` (see below).
+## How to use
+```python
+import cramming # needed to load crammed model
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("wilsonmarciliojr/crammed-bert-portuguese")
+model  = AutoModelForMaskedLM.from_pretrained("wilsonmarciliojr/crammed-bert-portuguese")
+text = "Oi, eu sou um modelo <mask>."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
 ## Training Details
+### Training Data & Config
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+- 30M entries from `TucanoBR/GigaVerbo`.
+- 107M sequences of 128 length.
+- tokenizer: WordPiece
+- vocab_size: 32768
+- seq_length: 128
+- include_cls_token_in_corpus: false
+- include_sep_token_in_corpus: true
 ### Training Procedure
+- **optim**:
+  -  type: AdamW
+  -  lr: 0.001
+  -  betas:
+    - 0.9
+    - 0.98
+  -  eps: 1.0e-12
+  -  weight_decay: 0.01
+  -  amsgrad: false
+  -  fused: null
+  - warmup_steps: 0
+  - cooldown_steps: 0
+  - steps: 900000
+  - batch_size: 8192
+  - gradient_clipping: 0.5
+- **objective**:
+  -  name: masked-lm
+  -  mlm_probability: 0.25
+  -  token_drop: 0.0
 #### Training Hyperparameters
+- num_transformer_layers: 16
+- hidden_size: 768
+-  intermed_size: 3072
+-  hidden_dropout_prob: 0.1
+-  norm: LayerNorm
+-  norm_eps: 1.0e-12
+-  norm_scheme: pre
+-  nonlin: GELUglu
+-  tie_weights: true
+-  decoder_bias: false
+-  sparse_prediction: 0.25
+-  loss: cross-entropy
+- **embedding**:
+  -  vocab_size: null
+  -  pos_embedding: scaled-sinusoidal
+  -  dropout_prob: 0.1
+  -  pad_token_id: 0
+  -  max_seq_length: 128
+  -  embedding_dim: 768
+  -  normalization: true
+  -  stable_low_precision: false
+- **attention**:
+  -  type: self-attention
+  -  causal_attention: false
+  -  num_attention_heads: 12
+  -  dropout_prob: 0.1
+  -  skip_output_projection: false
+  -  qkv_bias: false
+  -  rotary_embedding: false
+  -  seq_op_in_fp32: false
+  -  sequence_op: torch-softmax
+- **init**:
+  -  type: normal
+  -  std: 0.02
+-  ffn_layer_frequency: 1
+-  skip_head_transform: true
+-  use_bias: false
+-  **classification_head**:
+  - pooler: avg
+  -  include_ff_layer: true
+  -  head_dim: 1024
+  -  nonlin: Tanh
+  -  classifier_dropout: 0.1
+#### Speeds, Sizes, Times
+ - ~ 0.1674s per step (97886t/s)
 ## Evaluation
 <!-- This section describes the evaluation protocols and provides the results. -->
+TBD