Create ALBERT readme

README.md

@@ -0,0 +1,261 @@
+---
+language: en
+license: apache-2.0
+datasets:
+- bookcorpus
+- wikipedia
+---
+
+# ALBERT Base v2
+
+Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://arxiv.org/abs/1909.11942) and first released in
+[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference
+between english and English.
+
+Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by
+the Hugging Face team.
+
+## Model description
+
+ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
+was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
+publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
+was pretrained with two objectives:
+
+- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
+  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
+  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
+  GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
+  sentence.
+- Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
+classifier using the features produced by the ALBERT model as inputs.
+
+ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.
+
+## Intended uses & limitations
+
+You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
+be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=albert) to look for
+fine-tuned versions on a task that interests you.
+
+Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
+to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
+generation you should look at model like GPT2.
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='albert-base-v2')
+>>> unmasker("Hello I'm a [MASK] model.")
+[
+   {
+      "sequence":"[CLS] hello i'm a modeling model.[SEP]",
+      "score":0.05816134437918663,
+      "token":12807,
+      "token_str":"▁modeling"
+   },
+   {
+      "sequence":"[CLS] hello i'm a modelling model.[SEP]",
+      "score":0.03748830780386925,
+      "token":23089,
+      "token_str":"▁modelling"
+   },
+   {
+      "sequence":"[CLS] hello i'm a model model.[SEP]",
+      "score":0.033725276589393616,
+      "token":1061,
+      "token_str":"▁model"
+   },
+   {
+      "sequence":"[CLS] hello i'm a runway model.[SEP]",
+      "score":0.017313428223133087,
+      "token":8014,
+      "token_str":"▁runway"
+   },
+   {
+      "sequence":"[CLS] hello i'm a lingerie model.[SEP]",
+      "score":0.014405295252799988,
+      "token":29104,
+      "token_str":"▁lingerie"
+   }
+]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import AlbertTokenizer, AlbertModel
+tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
+model = AlbertModel.from_pretrained("albert-base-v2")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+and in TensorFlow:
+
+```python
+from transformers import AlbertTokenizer, TFAlbertModel
+tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2'')
+model = TFAlbertModel.from_pretrained("albert-base-v2)
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+### Limitations and bias
+
+Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
+predictions:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='albert-base-v2')
+>>> unmasker("The man worked as a [MASK].")
+
+[
+   {
+      "sequence":"[CLS] the man worked as a chauffeur.[SEP]",
+      "score":0.029577180743217468,
+      "token":28744,
+      "token_str":"▁chauffeur"
+   },
+   {
+      "sequence":"[CLS] the man worked as a janitor.[SEP]",
+      "score":0.028865724802017212,
+      "token":29477,
+      "token_str":"▁janitor"
+   },
+   {
+      "sequence":"[CLS] the man worked as a shoemaker.[SEP]",
+      "score":0.02581118606030941,
+      "token":29024,
+      "token_str":"▁shoemaker"
+   },
+   {
+      "sequence":"[CLS] the man worked as a blacksmith.[SEP]",
+      "score":0.01849772222340107,
+      "token":21238,
+      "token_str":"▁blacksmith"
+   },
+   {
+      "sequence":"[CLS] the man worked as a lawyer.[SEP]",
+      "score":0.01820771023631096,
+      "token":3672,
+      "token_str":"▁lawyer"
+   }
+]
+
+>>> unmasker("The woman worked as a [MASK].")
+
+[
+   {
+      "sequence":"[CLS] the woman worked as a receptionist.[SEP]",
+      "score":0.04604868218302727,
+      "token":25331,
+      "token_str":"▁receptionist"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a janitor.[SEP]",
+      "score":0.028220869600772858,
+      "token":29477,
+      "token_str":"▁janitor"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a paramedic.[SEP]",
+      "score":0.0261906236410141,
+      "token":23386,
+      "token_str":"▁paramedic"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a chauffeur.[SEP]",
+      "score":0.024797942489385605,
+      "token":28744,
+      "token_str":"▁chauffeur"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a waitress.[SEP]",
+      "score":0.024124596267938614,
+      "token":13678,
+      "token_str":"▁waitress"
+   }
+]
+```
+
+This bias will also affect all fine-tuned versions of this model.
+
+## Training data
+
+The ALBERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
+unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
+headers).
+
+## Training procedure
+
+### Preprocessing
+
+The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 30,000. The inputs of the model are
+then of the form:
+
+```
+[CLS] Sentence A [SEP] Sentence B [SEP]
+```
+
+### Training
+
+The ALBERT procedure follows the BERT setup.
+
+The details of the masking procedure for each sentence are the following:
+- 15% of the tokens are masked.
+- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
+- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
+- In the 10% remaining cases, the masked tokens are left as is.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
+
+|                | Average  | SQuAD1.1 | SQuAD2.0 | MNLI     | SST-2    | RACE     |
+|----------------|----------|----------|----------|----------|----------|----------|
+|V2              |
+|ALBERT-base     |82.3      |90.2/83.2 |82.1/79.3 |84.6      |92.9      |66.8      |
+|ALBERT-large    |85.7      |91.8/85.2 |84.9/81.8 |86.5      |94.9      |75.2      |
+|ALBERT-xlarge   |87.9      |92.9/86.4 |87.9/84.1 |87.9      |95.4      |80.7      |
+|ALBERT-xxlarge  |90.9      |94.6/89.1 |89.8/86.9 |90.6      |96.8      |86.8      |
+|V1              |
+|ALBERT-base     |80.1      |89.3/82.3 | 80.0/77.1|81.6      |90.3      | 64.0     |
+|ALBERT-large    |82.4      |90.6/83.9 | 82.3/79.4|83.5      |91.7      | 68.5     |
+|ALBERT-xlarge   |85.5      |92.5/86.1 | 86.1/83.1|86.4      |92.4      | 74.8     |
+|ALBERT-xxlarge  |91.0      |94.8/89.3 | 90.2/87.4|90.8      |96.9      | 86.5     |
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-1909-11942,
+  author    = {Zhenzhong Lan and
+               Mingda Chen and
+               Sebastian Goodman and
+               Kevin Gimpel and
+               Piyush Sharma and
+               Radu Soricut},
+  title     = {{ALBERT:} {A} Lite {BERT} for Self-supervised Learning of Language
+               Representations},
+  journal   = {CoRR},
+  volume    = {abs/1909.11942},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1909.11942},
+  archivePrefix = {arXiv},
+  eprint    = {1909.11942},
+  timestamp = {Fri, 27 Sep 2019 13:04:21 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-11942.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```