tf2crf
a crf layer for tensorflow 2 keras
Decision gist · record as of 2026-08-14
Yes-with-conditions. The package solves a real problem (CRF in Keras) and has low install friction, but it is abandoned and last tested with TensorFlow versions from 2021. Install only if you can verify compatibility with your TensorFlow and tensorflow-addons versions, and accept the risk that bugs or incompatibilities will not be fixed upstream. For active projects, consider whether a maintained alternative or a custom CRF implementation is safer.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TensorFlow >= 2.1.0 and compatible tensorflow-addons version; you must manage version alignment yourself.
- Low install friction; pure Python wheel.
- However, the package is abandoned—last release was 2021-09-10 and last commit 2023-03-25.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, making it safe to adopt from a licensing standpoint.
last release 2021-09-10 (1799 days) · last repo commit 2023-03-25 · 69 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 201,989 downloads/mo, #9,654 on PyPI
Alternatives
Verify before relying
pip install tf2crf
import tensorflow as tf
from tf2crf import CRF, ModelWithCRFLoss
from tensorflow.keras.layers import Input, Embedding, Bidirectional, GRU
from tensorflow.keras.models import Model
inputs = Input(shape=(None,), dtype='int32')
output = Embedding(100, 40, mask_zero=True)(inputs)
output = Bidirectional(GRU(64, return_sequences=True))(output)
crf = CRF(units=9, type='float32')
output = crf(output)
base_model = Model(inputs, output)
model = ModelWithCRFLoss(base_model, sparse_target=True)
model.compile(optimizer='adam')
model.fit(x=x, y=y, epochs=2, batch_size=2)- Whether the package works with current TensorFlow versions (last tested 2021-09-10)
- Compatibility status with recent tensorflow-addons releases
- Performance or correctness issues discovered since abandonment
What it is and what it does
tf2crf wraps a CRF (Conditional Random Field) layer into TensorFlow 2 Keras, allowing you to add sequence-level constraints to neural sequence labeling models. It handles masking automatically and supports mixed precision training. The package includes a ModelWithCRFLoss wrapper that decouples the loss function from the layer itself, and offers an optional DSC (Dice) loss variant for imbalanced datasets.
The layer is designed to sit at the end of a sequence model (typically after a BiLSTM or BiGRU) and learns transition probabilities between output labels. You build a standard Keras model, wrap it with ModelWithCRFLoss, and train normally. However, the package has been abandoned since mid-2021, so you should verify compatibility with your TensorFlow and tensorflow-addons versions before relying on it in production.
Use it for
- Named entity recognition (NER) tasks where label transitions matter and you want CRF constraints
- Part-of-speech tagging or other sequence labeling where forbidden transitions should be penalized
- Imbalanced NLP classification using the DSC loss variant to improve F1 scores
- Prototyping sequence models with Keras when you need CRF without building it from scratch
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes-with-conditions.
The package solves a real problem (CRF in Keras) and has low install friction, but it is abandoned and last tested with TensorFlow versions from 2021. Install only if you can verify compatibility with your TensorFlow and tensorflow-addons versions, and accept the risk that bugs or incompatibilities will not be fixed upstream. For active projects, consider whether a maintained alternative or a custom CRF implementation is safer.
Install
tf2crf on PyPI
Before you install
Low install friction; pure Python wheel. However, the package is abandoned—last release was 2021-09-10 and last commit 2023-03-25. Depends on tensorflow and tensorflow-addons, which are large dependencies; you'll need to manage compatibility between them yourself.
Requires TensorFlow >= 2.1.0 and compatible tensorflow-addons version; you must manage version alignment yourself.
License in practice
MIT License permits commercial and private use with minimal restrictions, making it safe to adopt from a licensing standpoint.
Quickstart
pip install tf2crf
import tensorflow as tf
from tf2crf import CRF, ModelWithCRFLoss
from tensorflow.keras.layers import Input, Embedding, Bidirectional, GRU
from tensorflow.keras.models import Model
inputs = Input(shape=(None,), dtype='int32')
output = Embedding(100, 40, mask_zero=True)(inputs)
output = Bidirectional(GRU(64, return_sequences=True))(output)
crf = CRF(units=9, type='float32')
output = crf(output)
base_model = Model(inputs, output)
model = ModelWithCRFLoss(base_model, sparse_target=True)
model.compile(optimizer='adam')
model.fit(x=x, y=y, epochs=2, batch_size=2)Verify before relying
- Whether the package works with current TensorFlow versions (last tested 2021-09-10)
- Compatibility status with recent tensorflow-addons releases
- Performance or correctness issues discovered since abandonment
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagestensorflowtensorflow-addons |
| Maintenance | Abandoned 1,799 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 201,989 / month, #9,654 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: tf2crf-0.1.33-py2.py3-none-any.whl
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See also sklearn-crfsuite · python-crfsuite · keras-nlp · tf-slim · tf-keras-nightly · seqeval · keras · tf-keras · keras-nightly · seqio-nightly